Vehicle Detection and Tracking in Car Video Based on Motion Model
This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the vid...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2011-06, Vol.12 (2), p.583-595 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Jazayeri, A Hongyuan Cai Jiang Yu Zheng Tuceryan, Mihran |
| description | This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras. |
| doi_str_mv | 10.1109/TITS.2011.2113340 |
| format | Article |
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This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. 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This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. 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| ispartof | IEEE transactions on intelligent transportation systems, 2011-06, Vol.12 (2), p.583-595 |
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| subjects | 1-D profiling Applied sciences Artificial intelligence Automobiles Automotive engineering Cameras Computer science control theory systems Dealing Dynamic target identification Exact sciences and technology feature detection Feature extraction hidden Markov model (HMM) Hidden Markov models Illumination in-car video Mathematical models Pattern recognition. Digital image processing. Computational geometry probability Real time Recognition Roads Target tracking Tracking vehicle motion Vehicles |
| title | Vehicle Detection and Tracking in Car Video Based on Motion Model |
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