5,395 research outputs found
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.Comment: has been published on IEEE TI
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework
People identification and tracking through fusion of facial and gait features
This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance
People identification and tracking through fusion of facial and gait features
This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance
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