Pedestrian tracking and detection have become critical aspects of advanced driver assistance systems (ADASs), due to their academic and commercial potential. Their objective is to locate various pedestrians in videos and assign them unique identities. The data association task is problematic, particularly when dealing with inter-pedestrian occlusion. This occurs when multiple pedestrians cross paths or move too close together, making it difficult for the system to identify and track individual pedestrians. Inaccurate tracking can lead to false alarms, missed detections, and incorrect decisions. To overcome this challenge, our paper focuses on improving data association in our pedestrian detection system’s Deep-SORT tracking algorithm, which is solved as a linear optimization problem using a newly generated cost matrix. We introduce a set of new data association cost matrices that rely on metrics such as intersections, distances, and bounding boxes. To evaluate trackers in real time, we use YOLOv5 to identify pedestrians in images. We also perform experimental evaluations on the Multiple Object Tracking 17 (MOT17) challenge dataset. The proposed cost matrices demonstrate promising results, showing an improvement in most MOT performance metrics compared to the default intersection over union (IOU) data association cost matrix.

Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.

The recent prominence of using infrared technology for human detection has garnered attention, particularly for applications in intelligent video surveillance and self-driving systems. This technology offers advantages in adverse weather conditions and night vision. However, within deep learning, the challenge of variable illumination during human detection persists. This study presents a novel method for identifying individuals in thermal images by enhancing the you only look once (YOLOv5s) algorithm. The approach incorporates the Bi-directional feature pyramid network (BiFPN) and the convolutional block attention module (CBAM) to improve the model’s feature integration and extraction capabilities. Evaluation on established thermal imaging datasets confirms the method’s superiority over state-of-the-art convolutional neural networks (CNN) based techniques, achieving remarkable precision 99.1% and recall 96.9%.