Activity Recognition In Videos
10 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Activity Recognition In Videos
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DAHLIA
InfiniteRep
Latest papers with no code
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition
We build a differentiable static-dynamic frequency mask prior to model the salient static and dynamic pixels in the video, crucial for the underlying task of action recognition.
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos
Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing.
Long Term Object Detection and Tracking in Collaborative Learning Environments
For a video of 23 minutes having resolution 858X480 @ 30 fps, the detection alone runs at 4. 7Xthe real-time, and the combined algorithm runs at 21Xthe real-time for an average IoU of 0. 84 and 0. 82, respectively.
Human Interaction Recognition Framework based on Interacting Body Part Attention
In this paper, we propose a novel framework that simultaneously considers both implicit and explicit representations of human interactions by fusing information of local image where the interaction actively occurred, primitive motion with the posture of individual subject's body parts, and the co-occurrence of overall appearance change.
Bubblenet: A Disperse Recurrent Structure To Recognize Activities
This paper presents an approach to perform human activity recognition in videos through the employment of a deep recurrent network, taking as inputs appearance and optical flow information.
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames.
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos
We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition.
A new network-based algorithm for human activity recognition in video
Based on this network, we further model people in the scene as packages while human activities can be modeled as the process of package transmission in the network.
