The week's pick
Reseach Article
Pedestrian Detection and Tracking based on Particle Filtering using HOG Features and Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 173 - Number 4 |
| Year of Publication: 2017 |
| Authors: Urooj Fatima, Jaykant Pratap Singh Yadav, Raj Kumar Goel |
10.5120/ijca2017915289
|
Urooj Fatima, Jaykant Pratap Singh Yadav, Raj Kumar Goel . Pedestrian Detection and Tracking based on Particle Filtering using HOG Features and Neural Network. International Journal of Computer Applications. 173, 4 ( Sep 2017), 30-32. DOI=10.5120/ijca2017915289
Abstract
This paper presents a neural network based approach for the detection and tracking of pedestrian. It addresses the problem of human detection and tracking in surveillance videos. This system consist of three major modules: Initially the video objects are detected using a novel temporal differencing based procedure and several mathematical morphology-based operations. On the basis of results, it was figured out that the Histogram of Oriented Gradient (HOG) and Relative Discriminative Histogram of Oriented Gradient (RDHOG) feature which were trained in the Neural Network classifiers have given a good performance within the expected process timing. Pedestrian tracking is the last part of the system. In this research we propose the tracking function which is based on the Particle filtering and a trustworthy pointing system. The movement and alteration in the size of the vehicles which are detected in continuous video frames are tracked by the function.
References
- WHO: Global status report on road safety 2009. Accessed on 1st Sept 2011. URL http://www.who.int/violence_injury_prevention/road_safetystatus/2009.
- Margie Peden et al. World report on road traffic injury prevention. Technical report, WHO, 2004.
- WHO: decade of action for road safety 2011-2020: Global launch. Accessed on 1st september 2011 launch.
- Olaf Gietelink. Design and Validation of Advanced Driver Assistance Systems. PhD thesis, Technical University of Delft, 2007.
- SooTeoh and Thomas Br¨aunl. Symmetry-based monocular vehicle detection system. Machine Vision and Applications, 2011, (DOI) 10.1007/s00138-011-0355-7.
- Sun Zehang, G. Bebis, and R. Miller. On-road vehicle detection: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):694–711, 2006.
- B. Steux, C. Laurgeau, L. Salesse, and D. Wautier. Fade: vehicle detection and tracking system featuring monocular color vision and radar data fusion. IEEE Intelligent Vehicle Symposium, 2002, volume 2, pages 632–639 vol.2, 2002.
- N. Srinivasa, Chen Yang, and C. Daniell. A fusion system for real-time forward collision warning in automobiles. Proceedings of the IEEE Intelligent Transportation Systems, volume 1, pages 457–462 vol.1, 2003.
- U. Kadow, G. Schneider, and A. Vukotich. Radar-Vision Based Vehicle Recognition with Evolutionary Optimized and Boosted Features. IEEE Intelligent Vehicles Symposium, 2007, pages 749–754, 2007.
- Sergiu Nedevschi, Andrei Vatavu, Florin Oniga, and Marc Michael Meinecke. Forward collision detection using a Stereo Vision System. 4th International Conference on Intelligent Computer Communication and Processing, 2008. (ICCP 2008), pages 115–122, 2008.
- Kunsoo Huh, Jaehak Park, Junyeon Hwang, and Daegun Hong. A stereo vision-based obstacle detection system in vehicles. Optics and Lasers in Engineering, 46(2):168–178, 2008.
- M. Bertozzi, A. Broggi, A. Fascioli, and S. Nichele. Stereo vision-based vehicle detection. Proceedings of the IEEE Intelligent Vehicles Symposium, 2000. (IV 2000), pages 39–44, 2000.
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