Yanming Chen
Anhui University
14 Papers
59 Citations
Yanming Chen is an academic researcher from Anhui University. The author has contributed to research in topics: Computer science & Kalman filter. The author has an hindex of 4, co-authored 9 publications. Previous affiliations of Yanming Chen include Beijing Institute of Technology.
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Papers
CCPrune: Collaborative channel pruning for learning compact convolutional networks
TL;DR: Wang et al. as mentioned in this paper proposed a method called collaborative channel pruning (CCPrune) to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors.
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Distributed Multi-Target Tracking Based on the K-MTSCF Algorithm in Camera Networks
TL;DR: A multi-target square-root cubature information weighted consensus filter (MTSCF) combined with a K-best joint probabilistic data association algorithm is proposed in this paper and demonstrates that the proposed approach performs favorably against the state-of-the-art methods in terms of accuracy and stability for tracking multiple targets in camera networks.
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An image-based near-duplicate video retrieval and localization using improved Edit distance
TL;DR: An image-based algorithm using improved Edit distance for near-duplicate video retrieval and localization and a detect-and-refine-strategy-based dynamic programming algorithm is proposed to generate the path matrix, which can be used to aggregate scores for video similarity measure and localize the similar parts.
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A Binarized Segmented ResNet Based on Edge Computing for Re-Identification.
TL;DR: The recently emerging edge computing and use the edge to combine the end devices and the cloud to implement the proposed binarized segmented ResNet and can greatly reduce the communication cost on the basis of basically not reducing the recognition accuracy of ReID.
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Combined discriminative global and generative local models for visual tracking
TL;DR: A compact global object representation is developed by extracting the low-frequency coefficients of the color and texture of the object based on two-dimensional discrete cosine transform and is incorporated into Bayesian inference framework.
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