TemporalNet: Real-time 2D-3D Video Object Detection

Abstract

Designing a video detection network based on state-of-the-art single-image object detectors may seem like an obvious choice. However, video object detection has extra challenges due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. We design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. To explore inter-frame information, we propose feature aggregation based on a temporal network. Our TemporalNet utilizes Appearance-preserving 3D convolution (AP3D) for extracting aligned features in the temporal dimension. Our temporal network functions at multiple scales for better performance, which allows communication between 2D and 3D blocks at each scale and also across scales. Our TemporalNet is a plug-and-play block that can be added to a multi-scale single-image detection network without any adjustments in the network architecture. When TemporalNet is applied to Yolov3 it is real-time with a running time of 35ms/frame on a low-end GPU. Our real-time approach achieves 77.1 % mAP (mean Average Precision) on ImageNet VID 2017 dataset with TemporalNet-4, where TemporalNet-16 achieves 80.9 % mAP which is a competitive result.