RoI Feature Propagation for Video Object Detection

TítuloRoI Feature Propagation for Video Object Detection
AutoresDaniel Cores, Manuel Mucientes and Víctor M. Brea
TipoComunicación para congreso
Fonte 24th European Conference on Artificial Intelligence, Santiago de Compostela, España, 2020.
AbstractHow to exploit spatio-temporal information in video to improve the object detection precision remains an open problem. In this paper, we boost the object detection accuracy in video with short- and long-term information. This is implemented with a two-stage object detector that matches and aggregates deep spatial features over short periods of time combined with a long-term optimization method that propagates detections' scores across long tubes. Short-time spatio-temporal information in neighboring frames is exploited by Region-of-Interest (RoI) temporal pooling. The temporal pooling works on linked spatial features through tubelets initialized from anchor cuboids. On top of that convolutional network, a double head processes both temporal and current frame information to give the final classification and bounding box regression. Finally, long-time information is exploited linking detections over the whole video from single detections and short-time tubelets. Our system achieves competitive results in the ImageNet VID dataset.