Statistical Analysis of Dynamic Actions

Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents

On gait as a biometric: progress and prospects

There is increasing interest in automatic recognition by gait given its unique capability to recognize people at a distance when other biometrics are obscured. Application domains are those of any noninvasive biometric, but with particular advantage in surveillance scenarios. Its recognition capability is supported by studies in other domains such as medicine (biomechanics), mathematics and psychology which also suggest that gait is unique. Further, examples of recognition by gait can be found in literature, with early reference by Shakespeare concerning recognition by the way people walk. Many of the current approaches confirm the early results that suggested gait could be used for identification, and now on much larger databases. This has been especially influenced by DARPA’s Human ID at a Distance research program with its wide scenario of data and approaches. Gait has benefited from the developments in other biometrics and has led to new insight particularly in view of covariates. Equally, gait-recognition approaches concern extraction and description of moving articulated shapes and this has wider implications than just in biometrics

Fast, invariant representation for human action in the visual system

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition

Methods for Recognizing Pose and Action of Articulated Objects with Collection of Planes in Motion

The invention comprises an improved system, method, and computer-readable instructions for recognizing pose and action of articulated objects with collection of planes in motion. The method starts with a video sequence and a database of reference sequences corresponding to different known actions. The method identifies the sequence from the reference sequences such that the subject in performs the closest action to that observed. The method compares actions by comparing pose transitions. The cross-homography invariant may be used for view-invariant recognition of human body pose transition and actions

P-CNN: Pose-based CNN Features for Action Recognition

This work targets human action recognition in video. While recent methods typically represent actions by statistics of local video features, here we argue for the importance of a representation derived from human pose. To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition. The descriptor aggregates motion and appearance information along tracks of human body parts. We investigate different schemes of temporal aggregation and experiment with P-CNN features obtained both for automatically estimated and manually annotated human poses. We evaluate our method on the recent and challenging JHMDB and MPII Cooking datasets. For both datasets our method shows consistent improvement over the state of the art.Comment: ICCV, December 2015, Santiago, Chil