A Comparison of Embedded Deep Learning Methods for PersonDetection

Chloe Eunhyang Kim1, Mahdi Maktab Dar Oghaz2, Jiri Fajtl2, Vasileios Argyriou2, PaoloRemagnino2

1VCA Technology Ltd, Surrey, United Kingdom2Kingston University, London, United Kingdom

chloe.kim@vcatechnology.com{m.maktabdaroghaz , j.fajtl , vasileios.argyriou , p.remagnino}@kingston.ac.uk

Keywords: Embedded systems, deep learning, object detection, convolutional neural network, person detection, YOLO;SSD; RCNN; R-FCN.

Abstract: Recent advancements in parallel computing, GPU technology and deep learning provide a new platform forcomplex image processing tasks such as person detection to flourish. Person detection is fundamental pre-liminary operation for several high level computer vision tasks. One industry that can significantly benefitfrom person detection is retail. In recent years, various studies attempt to find an optimal solution for persondetection using neural networks and deep learning. This study conducts a comparison among the state of theart deep learning base object detector with the focus on person detection performance in indoor environments.Performance of various implementations of YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessedusing our in-house proprietary dataset which consists of over 10 thousands indoor images captured form shop-ping malls, retails and stores. Experimental results indicate that, Tiny YOLO-416 and SSD (VGG-300) arethe fastest and Faster-RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectorsinvestigated in this study. Further analysis shows that YOLO v3-416 delivers relatively accurate result in areasonable amount of time, which makes it an ideal model for person detection in embedded platforms.

1 INTRODUCTION

The rise of industry 4.0, IoT and embedded systemspushes various industries toward data driven solutionsto stay relevant and competitive. In the retail indus-try, customer behavior analytic is one of the key el-ements of data driven marketing. Metrics such ascustomer’s age, gender, shopping habits and movingpatterns allow retailers to understand who their cus-tomers are, what they do and what they are lookingfor. these metrics also enables retailers to push cus-tomized and personalized marketing schemes to theircustomers across various stages of the customer life-cycle. Additionally, with the help of predictive mod-els, retailers are now enable to predict what their cus-tomers are likely to do in the future and gain edge overtheir competitors. In recent years, there has been anincreasing interest in the analysis of in-store customerbehavior. Retailers are looking for insights on in-storecustomer’s journey; Where do they go? What prod-ucts do they browse? and most importantly, whichproducts do they purchase (Ghosh et al., 2017) (Ma-jeed and Rupasinghe, 2017) (Balaji and Roy, 2017)?

Over the last decade, several tracking approachessuch as sensor based, optical based and radio basedhave been proposed. However, The majority of themare not efficient and reliable enough, or they expectsome form of interaction with customers which mightcompromise their shopping experience (Jia et al.,2016)(Foxlin et al., 2014). Analysis of in-store cus-tomer behavior through optical video signal recordedby security cameras has clear advantage over otherapproaches as it utilizes the existing surveillance in-frastructure and operates seamlessly with no interac-tion and interference with customers (Ohata et al.,2014)(Zuo et al., 2016). Despite the clear advan-tage of this approach, analysis of video signal re-quires complex and computationally expensive mod-els, which up until recent years, was impractical inthe real world. Recent advancements in parallel com-puting and GPU technology diminished this compu-tational barrier and allowed complex models such asdeep learning to flourish (Nickolls and Dally, 2010).

Aside from hardware limitations, classic com-puter vision and machine learning techniques hadhard time to model these complex patterns, however

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the rise of data driven approaches such as deep learn-ing, simplified these tasks, eliminating the need fordomain expertise and hard-core feature extraction. Areliable yet computationally reasonable person detec-tion model is fundamental requirement for in-storecustomer behavior analysis. Numerous studies fo-cused on person detection using deep neural networkmodels. However, none of which particularly focusedon the person detection in in door retail environments.Despite the similarity of these topics, there are a num-ber of unique challenges, such as lighting condition,camera angles, clutter and queues in retail environ-ments, which questions the adaptability of the exist-ing person detection solutions for retail environments.

In this regard, this research is mainly focused onperson detection as a preliminary step for in-storecustomer behavior modeling. We are particularly in-terested in evaluation and comparison of deep neu-ral network (DNN) person detection models in cost-effective, end-to-end embedded platforms such as theJetson TX2 and Movidius. State of the art deep learn-ing models use general purpose datasets such as PAS-CAL VOC or MS COCO to train and evaluate. De-spite their similarities, these dataset cannot be truerepresentative of the retail and store environments.In data driven techniques such as deep learning, thisadaptability issues are more pronounced than ever be-fore (LeCun et al., 2015). To address these issues, thisresearch investigates the performance of state of theart DNN models including variations of YOLO, SSD,RCNN, R-FCN and SqueezeDet in person detectionusing an in-house proprietary image dataset were cap-tured by conventional security cameras in retail andstores environments.

These images were manually annotated to formthe ground truth for training and evaluation of thedeep models. Having deep models trained by the sim-ilar type of images that could be found in target en-vironment, can significantly improve the accuracy ofthe models. However, preparation of a very large an-notated dataset is a big challenge. This research em-ploys average precision metric at various intersectionover union (IoU) as the figure of merit to comparemodel performance. As processing speed is a key fac-tor in embedded systems, this research also conductsa comprehensive comparison among the aforemen-tioned DNN techniques to find the most cost-effectiveapproach for person detection in embedded systems.

The major contributions of this study can be sum-marized as: first, integration and optimization of thestate of the art person detection algorithm into em-bedded platforms; second, an end-to-end comparativestudy among the existing person detection models interms of accuracy and performance and finally, a pro-

prietary dataset, which can be used in indoor humanand analysis studies.

The paper is organized as follow. Section 2 brieflydescribes the state of art object detection models usedin this research. Section 3 presents the overall frame-work, data acquisition process as well as experimentalsetup of the research. Section 4 describes the experi-mental results and discussions and finally, sections 5concludes the research.

2 CNN BASED OBJECTDETECTION

Various DNN based object detector have been pro-posed in the last few years. This research investi-gates the performance of state of the art DNN mod-els including variations of YOLO, SSD, RCNN, R-FCN and SqueezeDet in person detection. The mod-els have been trained using an in-house proprietaryimage dataset were captured by conventional securitycameras in retail and stores environments. The fol-lowing sections describes aforementioned DNN mod-els in more details.

2.1 RCNN Variants

The region-based convolutional neural network(RCNN) solution for object detection is quite straight-forward. This technique uses selective search to ex-tract just 2000 regions (region proposal) from the im-age and then, instead of trying to classify a huge num-ber of regions throughout the image only these 2000region will be investigated. Selective search initiallygenerates candidate regions, then uses a greedy al-gorithm to recursively combine similar regions intolarger ones. Finally, it uses the generated regions toproduce the final candidate region proposals. The re-gion proposals will be passed to a conventional neuralnetwork (CNN) for classification. Despite RCNN haslots of advantages over the conventional DNN objectdetector (Girshick et al., 2016), this technique is stillquite slow for any real-time application. Furthermore,a predefined threshold of 2000 region proposal cannotbe suitable for any given input image.

To address these limitations, other variants ofRCNN have been introduced (Ren et al., 2015). FasterRCNN is one popular variant of RCNN which mainlydevised to speed up RCNN. This algorithm elimi-nates the selective search algorithm used in the con-ventional RCNN and allows the network learn the re-gion proposals. The mechanism is very similar tofast RCNN where an image is provided as input to

a CNN to generate a feature map but, instead of us-ing a selective search algorithm on the feature mapto identify the region proposals, a separate network isused to predict region proposals. The predicted regionproposals are then reshaped using a region of interest(RoI) pooling layer and used to classify the image in-put within the proposed region (Ren et al., 2015). Totrain the Region Proposal Network, a binary class la-bel has been assigned to each anchor (1: being objectand 0: not object). Any with IoU over 0.7 determinesobject presence and anything below 0.3 indicates noobject exists. With these assumptions, we minimizean objective function following the multi-task loss inFast R-CNN which is defined as:

L({pi},{ti}) =1

Ncls∑

iNcls(pi, pi

∗)+

λ1

Nreg∑

iPi∗Lreg(ti, ti∗) (1)

where i is the index of anchor in the batch, pi is itspredicted probability of being an object; pi

∗ is theground truth probability of the anchor (1: representsobject, 0: represents non-object); ti is a vector whichdenotes the bounding box coordinates; ti∗ is groundtruth bounding box coordinates; Lcls is classificationlog loss and Lreg is regression loss. We have also de-ployed the Faster RCNN model using the Google in-ception framework which is expected to be less com-putational intensive.

2.2 R-FCN Variants

In contrast to the RCNN model which applies acostly per-region subnetwork hundreds of times, re-gion based fully convolutional network (R-FCN) isan accurate and efficient object detector that spreadsthe computation across the entire image. A position-sensitive score map is used to find a tradeoff be-tween translation-invariance in image classificationand translation-variance in object detection. Aposition-sensitive score defined as following:

r(i, j | θ) = ∑(x,y)∈bin(i, j)

zi, j,c(x+ x0,y+ y0 | θ)/n (2)

where rc(i, j) is the pooled response in the (i, j)th binin the cth category; zi, j,c is one score map out of thek2(C+1) score map; n in the number of the pixels inthe bin; (x0,u0) represents the top left corner of theregion of interest and θ denotes network learning pa-rameters. The loss function defined on each region ofinterest which calculated by summation of the crossentropy loss and box regression loss as following:

L(s, tx,y,w,h) = Lcls(sc∗)+λ[c∗ > 0]Lreg(t, t∗) (3)

where c∗ is the region of interest ground truth label;Lcls(sc∗) is cross entropy loss for classification; t∗ rep-resents the ground truth box and Lreg is the boundingbox regression loss. Aside from the original R-FCN,this study also investigates the R-FCN model with theGoogle inception framework (Dai et al., 2016).

2.3 YOLO Variants

You only look once (YOLO) is another state of the artobject detection algorithm which mainly targets realtime applications. it looks at the whole image at testtime and its predictions are informed by global con-text in the image. It also makes predictions with asingle network evaluation unlike models such RCNN,which require thousands for a single image. YOLOdivides the input image into an SxS grid. If the cen-ter of an object falls into a grid cell, that cell is re-sponsible for detecting that object. Each grid cell pre-dicts five bounding boxes as well as confidence scorefor those boxes. The score reflects how confident themodel is about the presence of an object in the box.For each bounding box, the cell also predicts a class.It gives a probability distribution score over all thepossible classes designate the object class. Combina-tion of the confidence score for the bounding box andthe class prediction, indicates the probability that thisbounding box contains a specific type of object. Theloss function is defined as:

λcoord

s2

∑i=0

B

∑j=0

1ob ji j [(xi− xi)

2 +(yi− yi)2]+

λcoord

s2

∑i=0

B

∑j=0

1ob ji j [(√

wi−√

wi)2 +(

√hi−

√hi)

2]+

s2

∑i=0

B

∑j=0

1ob ji j (Ci−Ci)

2 +λcoord

s2

∑i=0

B

∑j=0

1ob ji j (Ci−Ci)

2+

s2

∑i=0

1ob ji j ∑

c∈classes(pi(c)− pi(c))2

(4)

where 1ob ji indicates if object appears in cell i and

1ob ji j denotes the jth bounding box predictor in cell

i responsible for that prediction; x,y,w,h and C de-note the coordinates represent the center of the boxrelative to the bounds of the grid cell, the width andheight are predicted relative to the whole image andfinally C denotes the confidence prediction represents

the IoU between the predicted box and any groundtruth box. This study also investigates the other vari-ants of YOLO including YOLO-v2 as well as TinyYOLO models performance for person detection inretail environments (Redmon et al., 2016)(Redmonand Farhadi, 2017).

2.4 SSD Variants

Single shot multi-box detector (SSD) is one of thebest object detector in terms of speed and accuracy.The SSD object detector comprises two main stepsincluding feature maps extraction, and convolutionfilters application to detect objects. A predefinedbounding box (prior) is matched to the ground truthobjects based on IoU ratio. Each element of thefeature map has a number of default boxes associ-ated with it. Any default box with an IoU of 0.5 orgreater with a ground truth box is considered a match.For each box, the SSD network computes two criti-cal components including confidence loss which mea-sures how confident the network is at the presence ofan object in the computed bounding box using cat-egorical cross-entropy and location loss which com-putes how far away the networks predicted boundingboxes are from the ground truth ones based on thetraining data (Huang et al., 2017)(Liu et al., 2016).The overall loss function is defined as following:

L(x,c, l,g) =1N(Lcon f (x,c)+αLloc(x, l,g)) (5)

where N is the number of matched default boxes.Other variants of the standard SSD with 300 and 512inputs as well as MobileNet and Inception models hasbeen implemented and tested in this research (Howardet al., 2017)(Szegedy et al., 2015).

2.5 SqueezeDet

SqueezeDet is a real-time object detector used forautonomous driving systems. This model claimshigh accuracy as well as reasonable response latency,which are crucial for autonomous driving systems. In-spired by the YOLO, this model uses fully convolu-tional layers not only to extract feature maps, but alsoto compute the bounding boxes and predict the objectclasses. The detection pipeline of SqueezeDet onlycontains a single forward pass over the network, mak-ing it extremely fast (Wu et al., 2017). SqueezeDetcan be trained end-to-end, similarly to the YOLO andit shares similar loss function with YOLO object de-tection.

3 RESEARCH FRAMEWORK

Similar to any other machine learning task, this re-search employs training/testing and validation strat-egy to create the prediction models. All CNN modelswere trained and tested using our proprietary dataset.Predictions were compared against ground truth bymeans of cross entropy loss function to back propa-gate and optimize network weights, biases and othernetwork parameters. Finally, the trained models weretested against an unseen validation set to identify themodels performance in real life. Figure 1 shows over-all experimental framework.

Figure 1: Overall experimental framework

3.1 Data Acquisition

We have prepared a relatively large dataset com-prising total number of 10,972 image were mostlycaptured from CCTV cameras placed in departmentstores, shopping malls and retails. Majority of the im-ages were captured in indoor environments under var-ious conditions such as distance, lighting, angle, andcamera type. Given the fact that each camera has itsown color depth and temperature, field of view andresolution, all images passed through a preprocess-ing operation which ensures consistency across en-tire input data. Figure 2 shows some examples of ourdataset.

Figure 2: An example of tilt (left) and top-down (right)frame in dataset

In order to ease and speed up the annotation pro-cess, we have employed a semi-automatic annota-tion mechanism which uses a Faster RCNN incep-tion model to generate the initial annotations for each

Table 1: Average precision at IoU 0.95 and 0.50

# Model Framework AP[IoU=0.95]

AP[IoU=0.50]

1 Faster RCNN (ResNet-101) Tensorflow 0.245 0.4762 YOLOv3-416 Darknet 0.143 0.3673 Faster RCNN (Inception ResNet-v2) Tensorflow 0.317 0.5574 YOLOv2-608 Darknet 0.198 0.4635 Tiny YOLO-416 Darknet 0.035 0.1166 SSD (Mobilenet v1) Tensorflow 0.094 0.2337 SSD (VGG-300) Tensorflow 0.148 0.3078 SSD (VGG-500) Tensorflow 0.183 0.4039 R-FCN (ResNet-101) Tensorflow 0.246 0.48610 Tiny YOLO-608 Darknet 0.06 0.18511 SSD (Inception ResNet-v2) Tensorflow 0.116 0.26712 SqueezeDet Tensorflow 0.003 0.01213 R-FCN Tensorflow 0.124 0.319

given input image. The detection results were man-ually investigated and fine tuned to insure the relia-bility and integrity of the ground truth. Moreover,images with no person presence have been removedfrom the dataset. Finally, a random sampling processperformed over entire images. The final dataset con-sists of total number of 10,972 images no backgroundoverlap, divided into training set (5,790 images), test-ing set (2,152 images) and validation set (3,030 im-ages).

3.2 Experimental Setup

To measure and compare the average precision (AP)and IoU of the deep models, we have used a work-station powered by 16 GB of internal memory andNvidia GTX 1080ti graphics accelerator. To measureand compare the time complexity metrics, we haveutilized two common embedded platforms includingthe Nvidia Jetson TX2 as well as Movidius to run theexperiments.

4 EXPERIMENTAL RESULTSAND DISCUSSIONS

We investigated 13 different object detector deepmodels including variants of YOLO, SSD, RCNN,RFCN and SqueezeDet. To measure the accuracyof these models, we have used AP at two differentIoU ratios, including 0.5 which denotes a fair detec-tion and 0.95 which indicates a very accurate detec-tion. Table 2 summarizes the AP across various ob-ject detectors. It can be observed that, when IoU is0.95, Faster RCNN (Inception ResNet-v2) with aver-age precision of 0.317 outperforms other object de-tector in this research. Faster RCNN (ResNet-101)alongside R-FCN (ResNet-101) with respective AP of0.245 and 0.246 are among the best performers in thiscategory.

Table 2: Total latency of inference in both CPU and GPUmodes

# Model CPU Latency (S) GPULatency (S)

1 Faster RCNN (ResNet-101) 3.271 0.2322 YOLOv3-416 5.183 0.0173 Faster RCNN (Inception ResNet-v2) 10.538 0.4784 YOLOv2-608 11.303 0.0355 Tiny YOLO-416 1.018 0.0116 SSD (Mobilenet v1) 0.081 0.037 SSD (VGG-300) 0.361 0.0158 SSD (VGG-500) 0.968 0.0269 R-FCN (ResNet-101) 1.69 0.13110 Tiny YOLO-608 2.144 0.02511 SSD (Inception ResNet-v2) 0.109 0.0412 SqueezeDet 0.14 0.02713 R-FCN 3.034 0.084

On the other hand, SqueezeDet and Tiny YOLO-608 with respective AP of 0.003 and 0.06 performedpoorly in this category. Results with IoU = 0.50show a very similar trend. Once again, Faster RCNN(Inception ResNet-v2) with AP 0.557 outperformedother detector. R-FCN (ResNet-101), Faster RCNN(ResNet-101) and YOLOv2-608 with average preci-sion of 0.486, 0.476 and 0.463 respectively, are show-ing superior performance. In contrast, SqueezeDetand Tiny YOLO-416 with respective AP of 0.012 and0.116 generate poor results. Results also indicates,that, in terms of robustness and resiliency of the de-tector against increase in IoU, all models performroughly equally and there is no significant variance.Another noteworthy observation in this experiment isthe superiority of the Faster RCNN over other detec-tors that could be influenced biased by the approachused to prepared the ground truth. As we mentionedearlier in section 3.1, the dataset annotation initial-ized with the help of Faster RCNN inception modeldetector. Despite the significant manual adjustmentsand fine-tuning in annotation, we believe it introducessome level of bias to the results.

The time complexity of detectors were evaluatedwith measurement of execution latencies in two dif-ferent approaches. In the first approach total latencyof inference of a single test image has been measuredin both CPU and GPU modes. In the second approachthroughput of continuous inference with repeatingcamera capture. Table 3 shows the total latency of in-ference of a single test image on both CPU and GPU.Apparently, GPU is considerably faster than a CPUin matrix arithmetics such as convolution due to theirhigh bandwidth and parallel computing capabilities,but it is always interesting to learn this advantage ob-jectively. According to the results shown in table 3, inCPU mode, SqueezeDet, SSD (Inception ResNet-v2)and SSD (Mobilenet-v1) are the fastest deep modelsin this study.

These models benefit relatively simpler deep net-work with fewer arithmetic operations. This signifi-

cantly reduced their computational overhead and in-creased their performance. However, considering theAP result in table 2, it can be inferred that this per-formance gains, came with an expensive cost of ac-curacy and precision. Results in GPU mode shows avery similar trend however due to high bandwidth andthroughput of GPU, the variance in results are signif-icantly lower. According to Table 3, in GPU mode,SSD (VGG-300), Tiny YOLO-608, and SqueezeDetare among the fastest models in our experiments.Aside from CPU and GPU latency, we also measuredthe throughput of continuous inference with repeat-ing image feed. Due to several factors in the exper-imental setup and model architecture throughput ofcontinuous inference might not be necessarily corre-lated with the CPU and GPU latency. Figure 3 shows,Tiny YOLO-416 followed by SSD (VGG-300) withover 80 and 60 FPS respectively have the overall high-est throughput among the models investigated in thisstudy. On the other hand, Faster RCNN (InceptionResNet-v2) and Faster RCNN (ResNet-101) are slow-est in this regard. In order to deploy the deep modelsin embedded platforms, Caffe or Tensorflow modelsshould be optimized and restructured using MovidiusSDK or TensorRT. This enables the CNN model toutilize the target height/width effectively.

Figure 3: Throughput of continuous inference across vari-ous models

However, the supported layers by Movidius SDKor TensorRT are relatively basic and limited and com-plex models such as ResNet cannot be truly deployedin these platforms. As an example, leaky rectified lin-ear unit activation function in inception models is notsupported by the Jetson platform and cannot be fullyreplicated. Table 4 summarizes the throughput of con-tinuous inference across various deep models in em-bedded platforms. It can be observed that the NvidiaJetson performed significantly better than the Movid-ius across all different models. Furthermore, Ten-sorRT outperformed Caffe by a relatively large mar-gin. However, in terms of features and functionality,Caffe allows to reproduce more complex networks.

Table 3: Throughput of continuous inference across variousmodels using embedded platform including Movidius andJetson

# Model Framework Movidius Jetson

Caffe TensorRT

(FP16)Throughput

(FP32)Throughput

(FP16)Throughput

(FP32)Throughput

1 AgeNet Caffe 18 56 192 1272 AlexNet Caffe 10 37 65 543 GenderNet Caffe 18 62 198 1194 GoogleNet Caffe 9 19 120 735 SqueezeNet Caffe 17 37 166 1246 TinyYolo Caffe 7 19 -NA- -NA-7 Inception v1 Tensorflow 10 -NA- -NA- -NA-8 Inception v2 Tensorflow 7 -NA- -NA- -NA-9 Inception v3 Tensorflow 3 -NA- -NA- -NA-10 Mobilenet Tensorflow 19 -NA- -NA- -NA-

Finding the right deep model for embedded plat-form is not about accuracy neither performance butis about finding the right tradeoff between accuracyand performance, which satisfies the requirements.Deep models such as Tiny-YOLO can be extremelyfast. However, their accuracy is questionable. Fig-ure 4 plots the deep models Average Precision acrosstheir throughput. The closer to the top right cornerof the plot, the better the overall performance of themodel. Figure 4 shows among the various models thatwe investigated in this research, YOLO v3-416 andSSD (VGG-500) are the best tradeoff between Aver-age precision and throughput.

Figure 4: Average Precision [IoU=0.5] across throughput

5 CONCLUSION

Person detection is essential step in analysis of the in-store customer behavior and modeling. This study fo-cused on the use of DNN based object detection mod-els for person detection in indoor retail environmentsusing embedded platforms such as the Nvidia JetsonTX2 and the Movidius. Several DNN models includ-ing variations of YOLO, SSD, RCNN, R-FCN andSqueezeDet have been analyzed over our proprietarydataset that consists of over 10 thousands images interms of both time complexity and average preci-sion. Experiments results shows that Tiny YOLO-416 and SSD (VGG-300) are among the fastest mod-els and Faster RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate ones. How-

ever, neither of these models nail the tradeoff betweenspeed and accuracy. Further analysis indicates thatYOLO v3-416 delivers relatively accurate result inreasonable amount of time, which makes it a desirablemodel for person detection in embedded platforms.

ACKNOWLEDGEMENTS

We thank our colleagues from VCA Technology whoprovided data and expertise that greatly assisted theresearch. This work is co-funded by the EU-H2020within the MONICA project under grant agreementnumber 732350. The Titan X Pascal used for this re-search was donated by NVIDIA.

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