Multiclass Multiple Object Tracking

Abstract

Efficient and accurate object detection, classification and tracking have played a very important role in the advancement of computer vision systems. With the rise of deep learning techniques, the accuracy of visioning systems has increased a lot. This proposed work targets to incorporate state-of-the-art technology with the aim of achieving very high accuracy for real-time multiclass multiple object tracking with occlusion handling. A real-time multiclass multiple object tracker (MOT) is proposed using state-of-the-art object detection framework and deep convolutional neural networks. The proposed model is developed using YOLOv3 architecture for object detection and for real-time tracking, and a longer period of occlusion handling simple online and real-time tracking (SORT) is used as a base algorithm. The velocity and acceleration of objects along with their appearance information are used to predict the object location upon occlusion. Later detections are compared with MOT dataset detections and the YOLO bounding boxes, and based on their IOU matrix, normalized detections are provided.