ABSTRACT
Modern driver assistance systems enable a variety of use cases which rely on accurate localization information of all traffic participants. Due to the unavailability of satellite-based localization, the use of infrastructure cameras is a promising alternative in indoor spaces such as parking garages. This paper presents a parking management system which extends the previous work of the eValet system with a low-complexity tracking functionality on compressed video bitstreams (compressed-domain tracking). The advantages of this approach include the improved robustness to partial occlusions as well as a resource-efficient processing of compressed video bit-streams. We have separated the tasks into different modules which are integrated into a comprehensive architecture. The demonstrator setup includes a 2D visualizer illustrating the operation of the algorithms on a single camera stream and a 3D visualizer displaying the abstract object detections in a global reference frame.
- M. Ahad, J. Tan, H. Kim, and S. Ishikawa. 2012. Motion history image: its variants and applications. Machine Vision and Applications 23, 2 (2012), 255--281. Google ScholarDigital Library
- R.-V. Babu, M. Tom, and P. Wadekar. 2016. A survey on compressed domain video analysis techniques. Multimedia Tools and Applications 75, 2 (2016), 1043--1078. Google ScholarDigital Library
- D. Becker, J. Einsiedler, B. Schäufele, A. Binder, and I. Radusch. 2013. Identification of vehicle tracks and association to wireless endpoints by multiple sensor modalities. In International Conference on Indoor Positioning and Indoor Navigation. 1--10.Google Scholar
- D. Becker, A. Munjere, J. Einsiedler, K. Massow, F. Thiele, and I. Radusch. 2016. Blurring the border between real and virtual parking environments. In 2016 IEEE Intelligent Vehicles Symposium (IV). 1205--1210.Google Scholar
- D. Becker, B. Schäufele, J. Einsiedler, O. Sawade, and I. Radusch. 2014. Vehicle and pedestrian collision prevention system based on smart video surveillance and C2I communication. In Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. IEEE, 3088--3093.Google Scholar
- D. Becker, F. Thiele, O. Sawade, and I. Radusch. 2015. Cost-effective camera based ground truth for indoor localization. In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). 885--890.Google Scholar
- J. Einsiedler, D. Becker, and I. Radusch. 2014. External visual positioning system for enclosed carparks. In Positioning, Navigation and Communication (WPNC), 2014 11th Workshop on. IEEE, 1--6.Google Scholar
- J. Einsiedler, I. Radusch, and K. Wolter. 2017. Vehicle indoor positioning: A survey. In 2017 14th Workshop on Positioning, Navigation and Communications (WPNC). 1--6.Google Scholar
- A. Goldberg, S. Hed, H. Kaplan, P. Kohli, R. Tarjan, and R. Werneck. 2015. Faster and more dynamic maximum flow by incremental breadth-first search. In Algorithms-ESA 2015. Springer, 619--630.Google Scholar
- S. Gül, J. T. Meyer, C. Hellge, T. Schierl, and W. Samek. 2016. Hybrid video object tracking in H.265/HEVC video streams. In Multimedia Signal Processing (MMSP), 2016 IEEE 18th International Workshop on. IEEE, 1--5.Google Scholar
- A. Ibisch, S. Houben, M. Michael, R. Kesten, and F. Schuller. 2015. Arbitrary object localization and tracking via multiple-camera surveillance system embedded in a parking garage. In Video Surveillance and Transportation Imaging Applications 2015, Vol. 9407. International Society for Optics and Photonics, 94070G.Google Scholar
- A. Ibisch, S. Stümper, H. Altinger, M. Neuhausen, M. Tschentscher, M. Schlipsing, J. Salinen, and A. Knoll. 2013. Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded lidar sensors. In Intelligent Vehicles Symposium (IV), 2013 IEEE. IEEE, 829--834.Google Scholar
- S. Khatoonabadi and I. Bajić. 2013. Video object tracking in the compressed domain using spatio-temporal Markov random fields. IEEE Transactions on Image Processing 22, 1 (2013), 300--313. Google ScholarDigital Library
- R. Lienhart and J. Maydt. 2002. An extended set of Haar-like features for rapid object detection. In Proceedings. International Conference on Image Processing, Vol. 1. I-900--I-903 vol.1.Google Scholar
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779--788.Google Scholar
- B. Schäufele, O. Sawade, D. Becker, and I. Radusch. 2017. A transmission protocol for fully automated valet parking using DSRC. In Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual. IEEE, 636--637.Google Scholar
- T. Wiegand, G. Sullivan, G. Bjøntegaard, and A. Luthra. 2003. Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology 13, 7 (2003), 560--576. Google ScholarDigital Library
- Y. Wu, J. Lim, and M. Yang. 2015. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9 (2015), 1834--1848.Google ScholarDigital Library
- W. Zeng, J. Du, W. Gao, and Q. Huang. 2005. Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imaging 11, 4 (2005), 290--299. Google ScholarDigital Library
Index Terms
- Visual object tracking in a parking garage using compressed domain analysis
Recommendations
Visual Object Tracking Based on Mean-shift and Particle-Kalman Filter
Even though many algorithms have been developed and many applications of object tracking have been made, object tracking is still considered as a difficult task to accomplish. The existence of several problems such as illumination variation, tracking ...
Visual object tracking--classical and contemporary approaches
Visual object tracking (VOT) is an important subfield of computer vision. It has widespread application domains, and has been considered as an important part of surveillance and security system. VOA facilitates finding the position of target in image ...
Visual–inertial object tracking: Incorporating camera pose into motion models
AbstractVisual object tracking for autonomy of aerial robots could become challenging especially in the presence of target or camera fast motions and long-term occlusions. This paper presents a visual–inertial tracking paradigm by ...
Comments