Automation of Traffic Personnel Gesture Recognition

R Sathyaa, M Kalaiselvi Geethaa

aDepartment of Computer Science and Engineering, Annamalai University, India,Contact: rsathyamephd@gmail.com, geesiv@gmail.com

Human hand gesture recognition systems have a wide spread applications in video surveillance. In thispaper, traffic personnel hand gesture recognition is proposed. The n-frame cumulative difference is usedto extract the gesture information based on Cumulative Block Intensity Vector (CBIV). The experimentsare carried out on the real time traffic personnel hand gesture considering five actions viz., (to start oneside vehicles, to stop vehicles coming from the front, to stop approaching from behind, to stop vehiclesapproaching simultaneously from front and behind and to stop vehicles approaching simultaneously fromright and left) using Support Vector Machine (SVM) with polynomial and Radial Basis Function (RBF).The approach shows an overall good proposed performance of 92.65% using 25 dimension CBIV features for5-frame cumulative difference with RBF kernel when compared to polynomial kernel 87.62% in recognizingthe gesture performed.

Keywords : Action Recognition, Frame Difference, Gesture Recognition, Indian Traffic Signals, Sup-port Vector Machine.

1. INTRODUCTION

Indian traffic control is normally done in theform of human body gestures. Video surveil-lance is a hot topic and a great challenge in in-telligent transportation systems. Human handgestures integrated with vocal language and fa-cial expressions make the communication pro-cess more interactive and convenient for user.Limited resolution, self-occlusions, the degreesof freedom of a hand and the intra-class vari-ability of gestures render hand gesture recog-nition and detection as challenging problems.Automatic recognition of human gestures invideos is useful for surveillance.

Analyzing and recognizing human gesture invideos has been receiving increasing atten-tion of computer vision researchers from in-dustry. Intelligent Vehicles with the personnelin control are appearing to be more promis-ing, these observe the traffic situation and sup-port the driver by providing caution and ad-vice as needed. However, automatically recog-nizing human gestures in videos is challengingsince people can perform the same action indifferent ways with various execution speeds.

The human pose encapsulates useful informa-tion about the gesture action being performed,particularly on road traffic control by a trafficpersonnel which involve the safety of public aswell as the drivers.

1.1. Related WorkHuman Gesture analysis has been one of themost significant topics in computer vision, be-coming an essential part of many video surveil-lance systems. In [1] proposed representationthat keeps most of the shape details and thegait temporal variations. Experimental resultsof both synthesized and real database testifiedthat the frame difference energy image [FDEI]is a feasible gait representation.

In [2] proposed recognized human activitiesusing SVM multi-class classifier. They de-tect and locate people by non-parameter back-ground subtraction approach and extractedseveral features such as local ones from theminimum bounding boxes of human blobs ineach frames and a newly defined global onewith contour coding of the motion energy im-age. The popular sliding window approach [3]that generates ROIs over multiple scales from

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International Journal of Information Processing, 9(1), 67-76, 2015ISSN : 0973-8215IK International Publishing House Pvt. Ltd., New Delhi, India

Automated Gesture Recognition for road Traffic Surveillance 75

F-Measure computes some average of the infor-mation retrieval precision and recall metrics. Pis the proportion of the predicted positive casesthat were correct, as calculated using the equa-tion: P recision = T P/(T P + T R), Where TPand FP are True Positive and False Posi-tive. Recall (R) or Sensitivity or True PositiveRate (TPR) is the proportion of positive casesthat were correctly identified, as calculated us-ing the equation: Recall = T P/(T P + FN),where TP and FN are True Positive and FalseNegative.

In this experiment, 5-frame cumulative differ-ence with the sub block size is 5 × 5 using RBFkernel gives better performance. Table 2, Ta-ble 3 and Table 4 shows the accuracy resultson n-frame cumulative difference with 4 × 4,5 × 5 and 10 × 10 subblock size using SVMwith polynomial kernel. Table 5, Table 6 andTable 7 show the accuracy results on n-framecumulative difference with 4 × 4, 5 × 5 and10 × 10 subblock size using SVM with RBFkernel. Five actions are considered from realtime traffic personnel datasets as discussed inSection 4.1. The average performance of theproposed method for the 5 × 5 sub block sizewith 5-frame cumulative difference is 92.65%,which is given in Table 6. Table 8 gives theconfusion matrix of the 5-frame cumulative dif-ference for 5 × 5 sub block size using SVM withRBF kernel in CBIV feature.

4.3. Performance Obtained with n-frameCumulative Difference

The overall performance of the proposed ap-proach with n-frame cumulative difference onreal time traffic personnel action is shown inFigures 7 and 8. A recognition system withthe SVM with polynomial and RBF classifieris designed by the 5 actions of traffic personneldataset to evaluate the system work. The bestperformance is achieved by 25 dimension CBIVfeatures with 5-frame cumulative difference us-ing SVM with RBF kernel and average accu-racy as 92.65%. Figure 8 shows in overall per-formance of the proposed CBIV feature with n- frame cumulative difference using SVM poly-nomial kernel. Figure 9 shows in overall per-

formance of the proposed CBIV feature with n- frame cumulative difference using SVM withRBF kernel.

5. CONCLUSIONS AND FUTUREWORK

This work presented for Indian traffic personnelgesture recognition for traffic surveillance us-ing Cumulative Block Intensity Vector (CBIV)as feature. Indian traffic personnel performs12 actions, out of 12 actions; five actions aretaken performing the experiment. The ROIextracted from the various cumulative framedifference images are used for classification.These features evaluate the performance usingmulticlass SVM with polynomial and RBF ker-nel. This approach gives a good classificationaccuracy of 92.65% for 5-frame cumulative dif-ference with 25 dimension CBIV feature usingSVM (RBF) kernel. In future work, intend toenhance the flexibility of this approach by us-ing remaining seven traffic personnel actions torecognize and using this algorithm for all trafficpersonnel actions.

REFERENCES

1. Changhong Chen, Jimin Liang, Heng Zhao,Haihong Hu and Jie Tian. Frame DifferenceEnergy Image for Gait Recognition with In-complete Silhouettes, Pattern Recognition Let-

ters, pages 977–984, 2009.2. Huimin Qian, Yaobin Mao, Wenbo Xiang and

Zhiquan Wang. Recognition of Human Activi-ties using SVM Multi-class Classifier, Pattern

Recognition Letters, pages 100–111, 2010.3. Zhang L, Wu B and Nevatia R. Pedestrian

Detection in Infrared Images based on Lo-cal Shape Features, In Proceedings of IEEE

Conference on Computer Vision and Pattern

Recognition, pages 1–8, 2007.4. Encarnacion Folgado, Mariano Rincon, En-

rique J Carmona and Margarita Bachiller. ABlock based Model for Monitoring for HumanActivity, Neurocomputing, pages 1283–1289,2011.

5. Arunnehru J and Kalaiselvi Geetha M. Auto-matic Activity Recognition for Video Surveil-lance, International Journal of Computer Ap-

plications, 75(9), 2013.

76 Sathya R, et al.,

6. Shechtman E and Irani M. Matching LocalSelf-Similarities Across Images and Videos, In

IEEE Conference on Computer Vision and

Pattern Recognition, pages 1–8, 2007.7. Sathya R and Kalaiselvi geetha M. Vision

Based Traffic Police Hand Signal Recognitionin Surveillance Video: A Survey, International

Journal of Computer Applications, 81(9):1–10,2013.

8. Nazli Ikizler and Pinar Duygulu. Histogram ofOriented Rectangles: A New Pose Descriptorfor Human Action Recognition, Image and Vi-

sion Computing, pages 1515–1526, 2009.9. Qiong Liu, Jiajun Zhuang and Jun Ma. Robust

and Fast Pedestrian Detection Method for Far-Infrared Automotive Driving Assistance Sys-tems, Infrared Physics and Technology, pages288–299, 2013.

10. Wallraven C, Caputo B and Graf A. Recogni-tion with Local Features: Kernel Receipe, In

Proceedings of ICCV, pages 257–246, 2003.11. Wolf L and Shashua A. Kernel Principal Angles

for Classification Machines with Applicationsto Image Sequences Interpretation, In Proceed-

ings of CVPR, pages 635–640, 2003.12. Cristianini N and Shawe-Taylor J. An Intro-

duction to Support Vector Machines and OtherKernel-based Learning Methods, Cambridge

University Press, 2000.13. Vapnik V. Statistical Learning Theory, Wiley,

NY, 1998.14. Lewis J P. Tutorial on SVM, CGIT Labs, USC,

2004.15. Chang C C and Lin C J. LIBSVM: A Library

for Support Vector Machines, ACM Transac-

tions on Intelligent Systems and Technology,2:1–27, 2011.

R Sathya is currently workingas the Research Scholar onVideo Processing at AnnamalaiUniversity, Annamalai Nagar,India. She obtained her Bache-lor of Engineering in ComputerScience and Engineering fromAnnamalai University, India in

the year 2010. She recevied her Master degree inComputer Science and Engineering from Anna-malai University, India in the year 2012. Shejoined her Ph.D in Computer Science and Engi-neering, Annamalai University in the year 2012.She has published over 4 research papers in inter-

national journals and conferences. Her researchinterests include Video Processing and Patternclassification.

M KALAISELVI GEETHA

is currently working as AssociateProfessor in Dept. of ComputerScience and Engineering in An-namalai University, India. Shecompleted her B.E(CSE) fromInstitute of Road and TransportTechnology, Erode in the year

1991. She obtained her Masters degree in Com-puter Technology from IIT Delhi in the year 2001under Quality Improvement Programme (QIP).She gained her Ph.D in Computer Science andEngineering from Annamalai University in 2011.Her doctoral research topic is video classificationand indexing. She joined Annamalai Universityas Lecturer in 1994. She has published over 40research papers in international journals and con-ferences. She is now working on UGC sanctionedresearch project worth Rs. 13 lakhs on videosurveillance. Currently, six research scholars arepursuing their doctoral research under her su-pervision. Her research interests include VideoProcessing and Video surveillance, Pattern classi-fication and Artificial Intelligence.