Human activities as stochastic kronecker graphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Statistics of pairwise co-occurring local spatio-temporal features for human action recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Spatio-temporal video representation with locality-constrained linear coding
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
SuperFloxels: a mid-level representation for video sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
Shifted subspaces tracking on sparse outlier for motion segmentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Classifying web videos using a global video descriptor
Machine Vision and Applications
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Recognition of human actions in a video acquired by a moving camera typically requires standard preprocessing steps such as motion compensation, moving object detection and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. Therefore, action recognition from a moving camera is considered very challenging. In this paper, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned difficulties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we propose a novel approach based on low rank optimization, where we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets and two new aerial datasets called ARG and APHill, and obtained promising results.