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Ä®¸¸ ÇÊÅÍ È°¿ë ¿îÀüÀÚ º¸Á¶ ½Ã½ºÅÛ¿ë ij½ºÄÉÀ̵å Adaboost ¹× Adaptive ÀÎ½Ä ±â´É Â÷·® °¨Áö¿¬±¸
Ä®¸¸ ÇÊÅÍ È°¿ë ¿îÀüÀÚ º¸Á¶ ½Ã½ºÅÛ¿ë ij½ºÄÉÀ̵å Adaboost ¹× Adaptive ÀÎ½Ä ±â´É Â÷·® °¨Áö¿¬±¸
  • ÀúÀÚBaofeng Wang, Zhiquan Qi, Sizhong Chen, Zhaodu Liu, Guocheng Ma Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-12
  • µî·ÏÀÏ2020-12-21
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Vision-based vehicle detection is an important issue for advanced driver assistance
systems. In this paper, we presented an improved multi-vehicle detection and
tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with
target identity awareness. A cascade Adaboost classifier using Haar-like features
was built for vehicle detection, followed by a more comprehensive verification
process which could refine the vehicle hypothesis in terms of both location and
dimension. In vehicle tracking, each vehicle was tracked with independent identity
by an Adaptive Kalman filter in collaboration with a data association approach.
The AKF adaptively adjusted the measurement and process noise covariance
through on-line stochastic modelling to compensate the dynamics changes. The
data association correctly assigned different detections with tracks using global
nearest neighbour(GNN) algorithm while considering the local validation. During
tracking, a temporal context based track management was proposed to decide
whether to initiate, maintain or terminate the tracks of different objects, thus
suppressing the sparse false alarms and compensating the temporary detection
failures. Finally, the proposed method was tested on various challenging real
roads, and the experimental results showed that the vehicle detection performance
was greatly improved with higher accuracy and robustness.

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Á¦ 2Æí : ¿¬±¸³í¹®
Extended-Kalman-filter-based dynamic mode decomposition for
simultaneous system identification and denoising

1. Introduction 51
2. Cascade Adaboost for vehicle detection 54
3. Hypothesis generation 56
4. Multi-vehicle tracking with identity awareness 57
5. Adaptive Kalman filter 58
6. Experiments 63
7. Multi-vehicle tracking performance 65
8. References 70

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