Siamese tracking methods have recently drawn extensive attention due to their balanced accuracy and efficiency. However, most Siamese-based trackers use shallow backbone network, in which extracting high-level semanti...Siamese tracking methods have recently drawn extensive attention due to their balanced accuracy and efficiency. However, most Siamese-based trackers use shallow backbone network, in which extracting high-level semantic features is difficult. When the appearance of distractors and targets is particularly similar, these methods may lead to tracking drift or even failure. Considering this deficiency, we propose a Siamese network with enriched semantics, named ESDT. First, a semantic enrichment module(SEM) comprising dilated convolution layers is designed to improve the classification capability of the siamese tracker. In addition, the target template is updated adaptively to cope with the target texture information changes caused by illumination and blur and further promote the tracking performance. Finally, exhaustive experimental analysis on the public datasets shows that the proposed algorithm outperforms several state-of-the-art algorithms and could track the target stably despite disturbances.展开更多
For the correlation filtering(CF) tracking algorithm is not robust enough and cannot adapt to scale changes, target occlusion(OCC) and other complex interferences. We introduce a CF tracking algorithm based on superpi...For the correlation filtering(CF) tracking algorithm is not robust enough and cannot adapt to scale changes, target occlusion(OCC) and other complex interferences. We introduce a CF tracking algorithm based on superpixel and multifeature fusion(CFSMF). First, superpixel segmentation and clustering are performed for the target and its surrounding environment in the initial frame. Then, a target appearance is reconstructed through block segmentation-based overlapping analysis to remove redundant information. On this basis, the histogram of gradient(HOG) and HSI color features of the target sub-block are extracted to interact with their respective position filters. Accordingly, the target position is determined by the weighted fusion of the response values. In the scale prediction stage, we independently train a scale filter with a multiscale pyramid constructed at the estimated target location. The object scale is estimated in terms of the filter response, thereby enabling the tracking algorithm to adapt to the object scale change. Lastly, we introduce an OCC criterion for determining whether to update the model or not. Compared with the classical tracking algorithm kernelized correlation filters(KCF), the proposed algorithm boosts the tracking success rate by 20% and tracking accuracy by 15.9%. Our algorithm in this paper could track the target stably even when the target is occluded and its scale changes.展开更多
基金supported in part by the National Key Research and Development Project of China(No.2018YFB1601200)the Fundamental Research Funds for the Central Universities(No.3122018C004)。
文摘Siamese tracking methods have recently drawn extensive attention due to their balanced accuracy and efficiency. However, most Siamese-based trackers use shallow backbone network, in which extracting high-level semantic features is difficult. When the appearance of distractors and targets is particularly similar, these methods may lead to tracking drift or even failure. Considering this deficiency, we propose a Siamese network with enriched semantics, named ESDT. First, a semantic enrichment module(SEM) comprising dilated convolution layers is designed to improve the classification capability of the siamese tracker. In addition, the target template is updated adaptively to cope with the target texture information changes caused by illumination and blur and further promote the tracking performance. Finally, exhaustive experimental analysis on the public datasets shows that the proposed algorithm outperforms several state-of-the-art algorithms and could track the target stably despite disturbances.
基金This work has been supported in part by the National Key Research and Development Project of China(No.2018YFB1601200)the Key Projects of the Civil Aviation Joint Fund of the National Natural Science Foundation of China(No.U1533203)the Fundamental Research Funds for the Central Universities(No.3122018C004)。
文摘For the correlation filtering(CF) tracking algorithm is not robust enough and cannot adapt to scale changes, target occlusion(OCC) and other complex interferences. We introduce a CF tracking algorithm based on superpixel and multifeature fusion(CFSMF). First, superpixel segmentation and clustering are performed for the target and its surrounding environment in the initial frame. Then, a target appearance is reconstructed through block segmentation-based overlapping analysis to remove redundant information. On this basis, the histogram of gradient(HOG) and HSI color features of the target sub-block are extracted to interact with their respective position filters. Accordingly, the target position is determined by the weighted fusion of the response values. In the scale prediction stage, we independently train a scale filter with a multiscale pyramid constructed at the estimated target location. The object scale is estimated in terms of the filter response, thereby enabling the tracking algorithm to adapt to the object scale change. Lastly, we introduce an OCC criterion for determining whether to update the model or not. Compared with the classical tracking algorithm kernelized correlation filters(KCF), the proposed algorithm boosts the tracking success rate by 20% and tracking accuracy by 15.9%. Our algorithm in this paper could track the target stably even when the target is occluded and its scale changes.
