Adaptive pedestrian tracking via patch-based features and spatial-temporal similarity measurement
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- 作者：Xuewei ShenAuthor Vitae ; Xiubao Sui ; nusterwang@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Kechen PanAuthor Vitae ; Yuanrong TaoAuthor Vitae
- 关键词：PH ; perceptual hashing ; SVM ; support vector machine ; MIL ; multiple instance learning ; DCT ; discrete cosine transform ; HOG ; histogram of oriented gradient ; SAB ; Shadow Detection and Removal from a Single Image Using LAB Color Space ; ST ; Shadow Detection and Removal from a Single Image Using LAB Color Space ; ALSA ; Visual tracking via adaptive structural local sparse appearance model ; CT ; Real-time compressive tracking ; OMSIT ; Online multiple support instance tracking ; IVT ; Incremental learning for
- 刊名：Pattern Recognition
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：53
- 期：Complete
- 页码：163-173
- 全文大小：10680 K
文摘
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A patch-based appearance model is proposed according to stable properties.
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The number of valid patches in particles is automatically adjusted.
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A spatial–temporal similarity measurement is formulated for tracking and updating.
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A shadow removal algorithm makes tracking robustly under abrupt illumination.
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An extensive comparison with state-of-art methods.