Global-local attention with triplet loss and label smoothed crossentropy for person re-identification

Abstract

Person re-identification (Person Re-ID) is a research direction on tracking and identifying people in surveillance camera systems with non-overlapping camera perspectives. Despite much research on this topic, there are still some practical problems that Person Re-ID has not yet solved, in reality, human objects can easily be obscured by obstructions such as other people, trees, luggage, umbrellas, signs, cars, motorbikes. In this paper, we propose a multibranch deep learning network architecture. In which one branch is for the representation of global features and two branches are for the representation of local features. Dividing the input image into small parts and changing the number of parts between the two branches helps the model to represent the features better. In addition, we add an attention module to the ResNet50 backbone that enhances important human characteristics and eliminates irrelevant information. To improve robustness, the model is trained by combining triplet loss and label smoothing cross-entropy loss (LSCE). Experiments are carried out on datasets Market1501, and duke multi-target multi-camera (DukeMTMC) datasets, our method achieved 96.04% rank-1, 88,11% mean average precision (mAP) on the Market1501 dataset, and 88.78% rank-1, 78,6% mAP on the DukeMTMC dataset. This method achieves performance better than some state-of-the-art methods.

Improving night driving behavior recognition with ResNet50

“…The 'clipLimit' parameter was set to 3.0 to control the extent of contrast enhancement, with higher values resulting in more enhancement but requiring careful adjustment to prevent over-amplification. Additionally, a 'tileGridSize' of (8,8) was defined, dividing the image into 8×8 blocks for localized adaptive contrast adjustment. This division enabled CLAHE to adaptively equalize the histogram within each block, effectively enhancing visibility in regions with diverse lighting conditions.…”

“…It is critical in effectively detecting and assessing distinct eye characteristics, which are important indicators of attention. While convolutional neural networks (CNNs) excel at object detection and recognition [7], [8], their computing requirements make real-time, cost-effective processing on devices such as central processing units (CPUs) difficult. Furthermore, eyes are unique among facial features, necessitating a particular treatment.…”