MoCrop: Training Free Motion Guided Cropping for Efficient Video Action Recognition

Standard video action recognition models often process typically resized full frames, suffering from spatial redundancy and high computational costs. To address this, we introduce MoCrop, a motion-aware adaptive cropping module designed for efficient video action recognition in the compressed domain. Leveraging Motion Vectors (MVs) naturally available in H.264 video, MoCrop localizes motion-dense regions to produce adaptive crops at inference without requiring any training or parameter updates. Our lightweight pipeline synergizes three key components: Merge & Denoise (MD) for outlier filtering, Monte Carlo Sampling (MCS) for efficient importance sampling, and Motion Grid Search (MGS) for optimal region localization. This design allows MoCrop to serve as a versatile "plug-and-play" module for diverse backbones. Extensive experiments on UCF101 demonstrate that MoCrop serves as both an accelerator and an enhancer. With ResNet-50, it achieves a +3.5% boost in Top-1 accuracy at equivalent FLOPs (Attention Setting), or a +2.4% accuracy gain with 26.5% fewer FLOPs (Efficiency Setting). When applied to CoViAR, it improves accuracy to 89.2% or reduces computation by roughly 27% (from 11.6 to 8.5 GFLOPs). Consistent gains across MobileNet-V3, EfficientNet-B1, and Swin-B confirm its strong generality and suitability for real-time deployment. Our code and models are available at https://github.com/microa/MoCrop.