A Review of RealTime Object Detection and Tracking
Keywords:
Object Tracking, Object Recognition, Statistical Analysis, Object Detection, Background Subtraction, Performance Analysis, Optical FlowAbstract
Object detection and tracking is one of the critical areas of research due to routine change in motion of object and variation in scene size, occlusions, appearance variations, and ego-motion and illumination changes. Specifically, feature selection is the vital role in object tracking. It is related to many real time applications like vehicle perception, video surveillance and so on. In order to overcome the issue of detection, tracking related to object movement and appearance. Most of the algorithm focuses on the tracking algorithm to smoothen the video sequence. On the other hand, few methods use the prior available information about object shape, color, texture and so on. Tracking algorithm which combines above stated parameters of objects is discussed and analyzed in this research. The goal of this paper is to analyze and review the previous approach towards object tracking and detection using video sequences through different phases. Also, identify the gap and suggest a new approach to improve the tracking of object over video frame.
References
- Aggarwal, A., Biswas, S., Singh, S., Sural, S. & Majumdar, A.K., 2006. Object Tracking Using Background Subtraction and Motion Estimation in MPEG Videos, in 7th Asian Conference on Computer Vision. Springer- Verlag Berlin Heidelberg, pp. 121–130. doi:10.1007/11612704_13
- Chakravarthy, S., Aved, A., Shirvani, S., Annappa, M., & Blasch, E., 2015. Adapting Stream Processing Framework for Video Analysis. Procedia Comput. Sci. 51, 2648–2657. doi:10.1016/j.procs.2015.05.372
- Kothiya, S. V. & Mistree, K.B., 2015. A review on real-time object tracking in video sequences, in: 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, pp. 1–4. doi:10.1109/EESCO.2015.7253705
- Najva, N. & Bijoy, K.E., 2016. SIFT and Tensor-Based Object Detection and Classification in Videos Using Deep Neural Networks. Procedia Comput. Sci.93,351–358. doi:10.1016/j.procs.2016.07.220
- Oiwa, D., Fukui, S., Iwahori, Y., Nakamura, T. & Bhuyan, M.K., 2016. Tracking with Probabilistic Background Model by Density Forests, in: Proc. of IEEE/ACIS. IEEE, Okayama, Japan, pp. 391–396.
- Ramya, P. & Rajeswari, R., 2016. A Modified Frame Difference Method Using Correlation Coefficient for Background Subtraction. Procedia Comput. Sci. 93, 478–485. doi:10.1016/j.procs.2016.07.236
- Risha, K.P. & Kumar, A.C., 2016. Novel Method of Detecting Moving Object in Video. Procedia Technol. 24, 1055–1060. doi:10.1016/j.protcy.2016.05.235
- Sarkar, R., Bakshi, S. & Sa, P.K., 2012. A Real-time Model for Multiple Human Faces Tracking from Low-resolution Surveillance Videos. Procedia Technol. 6, 1004–1010. doi:http://dx.doi.org/10.1016/j.protcy.2012.10.122
- Soundrapandiyan, R. & Mouli, P.V.S.S.R.C., 2015. Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding. Procedia Comput. Sci. 58, 706–713. doi:10.1016/j.procs.2015.08.091
- Viswanath, A.,Kumari, R. & Senthamilarasu, V., 2015. Background Modelling from a Moving Camera. Procedia - Procedia Comput. Sci. 58, 289–296. doi:10.1016/j.procs.2015.08.023
- Weifeng Liu & Dacheng Tao, 2013. Multiview Hessian Regularization for Image Annotation. IEEE Trans. Image Process. 22, 2676–2687. doi:10.1109/TIP.2013.2255302
- Xue Mei & Haibin Ling, 2011. Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2259–2272. doi:10.1109/TPAMI.2011.66
- Zhang, J., Cao, J. & Mao, B., 2016. Moving Object Detection Based on Non- parametric Methods and Frame Difference for Traceability Video Analysis. Procedia Comput. Sci. 91, 995–1000. doi:10.1016/j.procs.2016.07.132
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