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Interactive autonomous applications require robustness of the perceptionengine to artifacts in unconstrained videos. In this paper, we examine theeffect of camera motion on the task of action detection. We develop a novelranking method to rank videos based on the degree of global camera motion. Forthe high ranking camera videos we show that the accuracy of action detection isdecreased. We propose an action detection pipeline that is robust to the cameramotion effect and verify it empirically. Specifically, we do actor featurealignment across frames and couple global scene features with localactor-specific features. We do feature alignment using a novel formulation ofthe Spatio-temporal Sampling Network (STSN) but with multi-scale offsetprediction and refinement using a pyramid structure. We also propose a novelinput dependent weighted averaging strategy for fusing local and globalfeatures. We show the applicability of our network on our dataset of movingcamera videos with high camera motion (MOVE dataset) with a 4.1% increase inframe mAP and 17% increase in video mAP.

RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems