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In this paper, we propose a new video object detector (VoD) method referredto as temporal feature aggregation and motion-aware VoD (TM-VoD), whichproduces a joint representation of temporal image sequences and object motion.The proposed TM-VoD aggregates visual feature maps extracted by convolutionalneural networks applying the temporal attention gating and spatial featurealignment. This temporal feature aggregation is performed in two stages in ahierarchical fashion. In the first stage, the visual feature maps are fused ata pixel level via gated attention model. In the second stage, the proposedmethod aggregates the features after aligning the object features usingtemporal box offset calibration and weights them according to the cosinesimilarity measure. The proposed TM-VoD also finds the representation of themotion of objects in two successive steps. The pixel-level motion features arefirst computed based on the incremental changes between the adjacent visualfeature maps. Then, box-level motion features are obtained from both the regionof interest (RoI)-aligned pixel-level motion features and the sequentialchanges of the box coordinates. Finally, all these features are concatenated toproduce a joint representation of the objects for VoD. The experimentsconducted on the ImageNet VID dataset demonstrate that the proposed methodoutperforms existing VoD methods and achieves a performance comparable to thatof state-of-the-art VoDs.

Joint Representation of Temporal Image Sequences and Object Motion for Video Object Detection