Histogram of Oriented Principal Components for Cross-View Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods

Hierarchical transfer learning for online recognition of compound actions

Recognising human actions in real-time can provide users with a natural user interface (NUI) enabling a range of innovative and immersive applications. A NUI application should not restrict users’ movements; it should allow users to transition between actions in quick succession, which we term as compound actions. However, the majority of action recognition researchers have focused on individual actions, so their approaches are limited to recognising single actions or multiple actions that are temporally separated. This paper proposes a novel online action recognition method for fast detection of compound actions. A key contribution is our hierarchical body model that can be automatically configured to detect actions based on the low level body parts that are the most discriminative for a particular action. Another key contribution is a transfer learning strategy to allow the tasks of action segmentation and whole body modelling to be performed on a related but simpler dataset, combined with automatic hierarchical body model adaption on a more complex target dataset. Experimental results on a challenging and realistic dataset show an improvement in action recognition performance of 16% due to the introduction of our hierarchical transfer learning. The proposed algorithm is fast with an average latency of just 2 frames (66ms) and outperforms state of the art action recognition algorithms that are capable of fast online action recognition

Group context learning for event recognition

We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, fol-lowing, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that repre-sents the probabilistic group membership of the individuals. We then extract features from this graph to capture the mo-tion and action contexts among the groups. The features are represented using the “bag-of-words ” scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time. 1

Transferring a generic pedestrian detector towards specific scenes

The performance of a generic pedestrian detector may drop significantly when it is applied to a specific scene due to mismatch between the source dataset used to train the detector and samples in the target scene. In this paper, we investigate how to automatically train a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. It tackles the problem from three aspects. (1) With a graphical represen-tation and through exploring the indegrees from target sam-ples to source samples, the source samples are properly re-weighted. The indegrees detect the boundary between the distributions of the source dataset and the target dataset. The re-weighted source dataset better matches the target scene. (2) It takes the context information from motions, scene structures and scene geometry as the confidence scores of samples from the target scene to guide trans-fer learning. (3) The confidence scores propagate among samples on a graph according to the underlying visual structures of samples. All these considerations are formu-lated under a single objective function called Confidence-Encoded SVM. At the test stage, only the appearance-based detector is used without the context cues. The effectiveness of the proposed framework is demonstrated through experi-ments on two video surveillance datasets. Compared with a generic pedestrian detector, it significantly improves the de-tection rate by 48 % and 36 % at one false positive per image on the two datasets respectively. 1