Video-based Person re-identification with parallel correction and fusion of pedestrian area features

Abstract

<abstract><p>Deep learning has provided powerful support for person re-identification (person re-id) over the years, and superior performance has been achieved by state-of-the-art. While under practical application scenarios such as public monitoring, the cameras' resolutions are usually 720p, the captured pedestrian areas tend to be closer to $ 128\times 64 $ small pixel size. Research on person re-id at $ 128\times 64 $ small pixel size is limited by less effective pixel information. The frame image qualities are degraded and inter-frame information complementation requires a more careful selection of beneficial frames. Meanwhile, there are various large differences in person images, such as misalignment and image noise, which are harder to distinguish from person information at the small size, and eliminating a specific sub-variance is still not robust enough. The Person Feature Correction and Fusion Network (FCFNet) proposed in this paper introduces three sub-modules, which strive to extract discriminate video-level features from the perspectives of "using complementary valid information between frames" and "correcting large variances of person features". The inter-frame attention mechanism is introduced through frame quality assessment, guiding informative features to dominate the fusion process and generating a preliminary frame quality score to filter low-quality frames. Two other feature correction modules are fitted to optimize the model's ability to perceive information from small-sized images. The experiments on four benchmark datasets confirm the effectiveness of FCFNet.</p></abstract>

Heuristic optimization and deep learning models for multimodal pedestrian detection

“…For multi-scale features, a cross-scale feature fusion module is designed which provides rich semantic features with the help of Feature Pyramid Network (FPN) for accurate target localization. In the neck network [15], we can see three different scales of feature fusion components with the dimensions of 76x76x255, 38x38x255 and 19x19x255, where "255" represents the range of image intensities in the network. In addition, the CSP (Cross Stage Partial) network replaces the traditional residual unit by CBL (Convolution, BatchNorm, LeakyReLU) module.The SPP (Spatial Pyramid Pooling) module combines the advantages of maximum pooling with the adaptability to different kernel sizes.…”