Evaluation of Object Detectors in Online Videos

Abstract

Object detectors based on deep convolutional networks generally have problems such as large amount of calculation, accuracy and speed of object detecor cannot have both, and it is difficult to achieve real-time object detection on embedded and mobile devices for online videos. Therefore, evaluating existing object detectors is an important issue that needs to be faced by a wide range of object detection applications. This paper proposes an evaluation method for object detectors in online video scenes. The method comprehensively considers the factors of the law of appeared objects, camera parameters, accuracy and speed of object detectors, and uses a combination of mathematical analysis and simulation experiments. On the Nvidia Jetson TX2 platform, the current major objects detectors have been analyzed and evaluated in detail using our evaluation method. The main evaluation results show that the YOLOv3 family is better than other object detectors and their variants such as SSD and RetinaNet, although it has not the highest accuracy, its fastest speed helps to occupy an advantage in the (different) object detection rate.