MaskVD: Region Masking for Efficient Video Object Detection

Sreetama Sarkar, Gourav Datta, Souvik Kundu, Kai Zheng, Chirayata Bhattacharyya, Peter A. Beerel; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1955-1964

Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications particularly for tasks that require state-of-the-art Vision Transformers (ViTs). Several research efforts have tried to address this challenge by leveraging the fact that large portions of the video undergo very little change across frames leading to redundant computations in frame-based video processing. In particular some works leverage pixel or semantic differences across frames however this yields limited latency benefits with significantly increased memory overhead. This paper in contrast presents a strategy for masking regions in video frames that leverages the semantic information in images and the temporal correlation between frames to significantly reduce FLOPs and latency with little to no penalty in performance over baseline models. In particular we demonstrate that by leveraging extracted features from previous frames ViT backbones directly benefit from region masking skipping up to 80% of input regions improving FLOPs and latency by 3.14x and 1.5x. We improve memory and latency over the state-of-the-art (SOTA) by 2.3x and 1.14x while maintaining similar detection performance. Additionally our approach demonstrates promising results on convolutional neural networks (CNNs) and provides latency improvements over the SOTA up to 1.3x using specialized computational kernels. Code is available at: https://github.com/sreetamasarkar/MaskVD