Cars tracking based on YOLO for feature extraction
Younis Al-Arbo, Khalil Alsaif · AIP conference proceedings · 2023
Object tracking is regarded as one of the significant topics in the scope of computer vision, which led to rapid development in the practical field through enhancing the reliable tools available in such a field. Recently, the appearance of Artificial Neural Networks (ANNs) resulted in new methods to the identification and recognition of objects. In this research a noble method is suggested for tracking objects inside video files, the method was used to detect cars on the streets through extracting the features of these cars by using You Only Look Ones (YOLO v3) Our method was able to process the extracted features of the cars at detection phase. Also, it exploited the extracted feature in an effective way in order to obtain precise detections. The findings show that our proposed method performs better than the other methods available in the field, as it is able to produce better predictions by using less computation which resulted in reducing the time that such process usually takes. The evaluation results show that our method was able to process an average of 207.6 frames per second to track objects with 67.6% Multi-Object Tracking Accuracy (MOTA) and 89.1% Multi-Object Tracking Precision (MOTP).