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Object detection is one of the most important challenges in computer vision.Object detectors are usually trained on bounding-boxes from still images.Recently, video has been used as an alternative source of data. Yet, for agiven test domain (image or video), the performance of the detector depends onthe domain it was trained on. In this paper, we examine the reasons behind thisperformance gap. We define and evaluate different domain shift factors: spatiallocation accuracy, appearance diversity, image quality and aspect distribution.We examine the impact of these factors by comparing performance before andafter factoring them out. The results show that all four factors affect theperformance of the detectors and their combined effect explains nearly thewhole performance gap.

Analysing domain shift factors between videos and images for object detection