基于通道特征聚合的行人重识别算法

在基于深度学习的行人重识别算法中，通道特征易被忽视而导致模型表达能力降低。为此，以ResNeSt50为骨干网络，借鉴SENet通道注意力特点在残差块末尾接入SE block，增强网络对通道特征的提取能力；针对ReLU函数因缺少控制因子而限制不同通道特征图对激活值的准确响应问题，引入一个动态学习因子来丰富通道特征权重信息，以形成新的加权激活函数Weighted ReLU(WReLU)；基于分组卷积特征图局部而设计新的激活函数Leaky Weighted ReLU(LWReLU)，有效提高不同位置的深度特征表达能力；在Split-Attention和SE block中应用LWReLU，改善Split-Attention对各组特征图的权重学习能力；利用circle loss改进损失函数，优化目标收敛过程，从而提高模型精度。实验结果表明：在CUHK03-NP、Market1501和DukeMTMC-ReID数据集上，所提方法的Rank-1比原骨干网络分别提高了19.08%、0.98%、2.02%，且其m AP比原骨干网络分别提高了17.13%、2.11%、2.56%。

Person Re-identification Algorithm Based on Channel Feature Aggregation

In deep-learning person re-identification algorithms, channel characteristics may be neglected, leading to a degraded model-expression ability. Address to the problem, we choose the ResNeSt50 as backbone network, and add an SE block to the end of residual blocks by using characteristics of SENet channel attention for enhancing features extraction of channels in networks. In addition, due to lack of control factors, ReLU function may reduce the correct responses of different feature graphs to activation values. Thus, we present two new activation functions. One is named as Weighted ReLU (WReLU) by combining ReLU with weight bias term, which can effectively improve feature selection ability in neural networks, and the other is Leaky Weighted ReLU (LWReLU), which is applied in Split-Attention and SE block, and enables Split-Attention to promote the weight learning ability from feature maps. Moreover, a new loss function with circle loss is also proposed for optimizing the convergence of objective function. Experimental results show that the proposed algorithm outperforms original backbone by 19.08%, 0.98%, and 2.02% in Rank-1, and 17.13%, 2.11%, and 2.56% in mAP respectively on CUHK03-NP, Market1501, and DukeMTMC-ReID datasets.