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Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking
- Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking
- Lim, YC[Lim, Young-Chul]; Kim, J[Kim, Jonghwan]; Lee, CH[Lee, Chung-Hee]; Lee, M[Lee, Minho]
- DGIST Authors
- Lim, YC[Lim, Young-Chul]; Kim, J[Kim, Jonghwan]; Lee, CH[Lee, Chung-Hee]
- Issue Date
- International Journal of Advanced Robotic Systems, 10
- Article Type
- Accuracy and Precision; Bayesian Filter; Bayesian Filters; Data Association; Monte-Carlo Method; Multiple Hypotheses; Multiple Hypothesis; Multiple Object Tracking; Obstacle Detection; Obstacle Detectors; Signal Filtering and Prediction; Similarity Functions; Stereo Vision; Tracking (Position); Vehicle Recognition
- In this paper, we present a tracking-by-multiple hypotheses framework to detect and track multiple vehicles accurately and precisely. The tracking-bymultiple hypotheses framework consists of obstacle detection, vehicle recognition, visual tracking, global position tracking, data association and particle filtering. The multiple hypotheses are from obstacle detection, vehicle recognition and visual tracking. The obstacle detection detects all the obstacles on the road. The vehicle recognition classifies the detected obstacles as vehicles or non-vehicles. 3D feature-based visual tracking estimates the current target state using the previous target state. The multiple hypotheses should be linked to corresponding tracks to update the target state. The hierarchical data association method assigns multiple tracks to the correct hypotheses with multiple similarity functions. In the particle filter framework, the target state is updated using the Gaussian motion model and the observation model with associated multiple hypotheses. The experimental results demonstrate that the proposed method enhances the accuracy and precision of the region of interest. © 2013 Lim et al.
- SAGE Publications Ltd
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