Kernelized Correlation Filter (KCF) algorithm has been successfully applied in object tracking. By finding the maximum confidence in the search region, the candidate target block is determined by KCF with constant size. When the scale of the target changes during its actual movement, the traditional KCF algorithms often fail to track the target. Moreover, it is difficult to judge whether the object is missing due to the lack of self-adaptive threshold regulation scheme in the KCF tracking. In order to solve these problems, this paper proposes a scale-adaptive target tracking algorithm based on KCF, which is mainly divided into the following steps. Firstly, the positive and negative samples of the nearest neighbor classifier are initialized by the selected target and its surrounding non-target areas. Secondly, the peak point of the spectral response of the current frame image is obtained by executing the KCF algorithm, which is the target center point. Thirdly, the scale change of object is obtained by calculating the ratio of the bandwidth of spectral response peak centered candidate regions and the target in the previous frame. Fourthly, the scaled candidate target is confirmed by calculating the sample similarity between it and the Nearest Neighbor Classifier (NNC). Finally, the positive and negative samples of the nearest neighbor classifier are updated with the confirmed tracking target and non-target respectively. Extensive experiments have been carried on four test video sequences. The experimental results show that our proposed method achieves a higher success rate and accuracy with less running time compared with the state-of-the- art methods.
KEYWORDS: Detection and tracking algorithms, Remote sensing, Data modeling, Principal component analysis, Statistical modeling, Image compression, Cameras, Data storage, Surveillance, Video surveillance
Person re-identification (Re-ID) is an important technique towards the automatic search of a person’s presence in a surveillance video or security systems. Applying incremental learning techniques to accelerate the online training speed with ever-increasing data is desired and critical for Re-ID. As an incremental learning algorithm, Incremental Kernel Null Foley-Sammon Transform (IKNFST) method significantly reduces the computational complexity while holds the accuracy. However, with ever-increasing person samples within the same category, the corresponding growth of dimensions makes it difficult to update the online model. To address the issue, we propose to maintain constant update speed by constructing Reduce Set (RS) expansions during online updating. The key idea is to firstly extract new information brought by newly-added samples and integrate it with the existing model by Incremental Kernel Principal Component Analysis (IKPCA) scheme for further Reduce Set (RS) compression. And the compressed samples and the corresponding model are then input to Kernel Null Foley-Sammon Transform (KNFST) algorithm for generating an updated model. Extensive experiments have been carried on three public datasets, including Market-1501, DukeMTMCReID and CUHK03. The results show that our proposed method beats the state-of-the-art IKNFST by a big margin.
Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on customized spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that dehazed edge images have richer detail than before restoration, an edge cost function is added to the ransmission model. The experimental results demonstrate that the proposed method provides higher dehazing quality and lower time complexity than the previous technique.
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