This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain a greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.
Person re‐identification (re‐id), with the goal to recognize persons from images captured by nonoverlapping cameras, is a challenging topic in computer vision. It has been studied extensively in recent years, and the attention mechanism is widely applied in person re‐id. But many works mainly focus on extracting discriminative features from local saliency regions, while ignoring some potentially global information between whole‐body features and body‐part features. In this study, we first proposed two effective global information for extracting discriminative features: spatial topology information (STI) and channel affinity information (CAI). On this basis, we further propose a Multi‐information Fusion reinforced Global Attention (MIFGA) module which can effectively fuse a variety of information and utilize more comprehensive information to guide the learning of attention, so as to obtain pedestrian features that are conducive to clustering. Specifically, the proposed MIFGA module includes spatial attention (MIFGA‐S) and channel attention (MIFGA‐C). MIFGA‐S mainly utilizes local feature semantic information and STI to guide the learning of spatial attention. Furthermore, to mine the potential topology information in original feature maps, the self‐learning graph convolution network is proposed. MIFGA‐C fuses channel semantic information and CAI to guide the learning of channel attention. Extensive ablation studies demonstrate that our proposed MIFGA significantly enhances the baseline model and achieves a competitive performance compared with the state‐of‐the‐art person re‐id methods on standard data sets Market‐1501, DukeMTMC‐reID, and CUHK03.
Person re-identification has become a challenging task due to various factors. One key to effective person re-identification is the extraction of the discriminative features of a person's appearance. Most previous works based on deep learning extract pedestrian characteristics from neural networks but only from the top feature layer. However, the low-layer feature could be more discriminative in certain circumstances. Hence, we propose a method, named the multi-level feature network with multiple losses (MFML), which has a multi-branch network architecture that consists of multiple middle layers and one top layer for feature representations. To extract the discriminative middle-layer features and have a good effect on deeper layers, we utilize the triplet loss function to train the middle-layer features. For the top layer, we focus on learning more discriminative feature representations, so we utilize the hybrid loss (HL) function to train the toplayer feature. Instead of concatenating multilayer features directly, we concatenate the weighted middlelayer features and the weighted top-layer feature as the discriminative features in the testing phase. The extensive evaluations conducted on three datasets show that our method achieves a competitive accuracy level compared with the state-of-the-art methods.
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