Efficient Hidden Semi-Markov Model Inference for Structured Video Sequences

The 2013 International Joint Conference on Neural Networks (IJCNN)

et al. 2013

Abstract-Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Section: B Results and Discussionmentioning

confidence: 99%

Human behaviour recognition based on trajectory analysis using neural networks

The 2013 International Joint Conference on Neural Networks (IJCNN)

et al. 2013

Section: Discussionmentioning

confidence: 99%

“…Using predefined models for temporal sequence recognition usually requires the design of models for each behaviour derived from short-term activity patterns. For example, a windowshopping behaviour could be modelled as a sequence of states of actions in middle level of understanding as moving, browsing, moving and, finally, entering a shop [23]. Furthermore, it requires the specification of semantic understanding of low and middle level actions.…”

Section: Prediction Of Human Behaviourmentioning

confidence: 99%

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

et al. 2015

Neural Process Lett

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

“…Sensitivity and specificity results of context classification have been calculated from reported success rates in [11], [39] and [21] of comparable experiments on the Corridor dataset (results for the other datasets are not available). These methods are grouped as state and semantic models using predefined models and rules to evaluate behaviours.…”

Section: Methodsmentioning

confidence: 99%

Constrained self-organizing feature map to preserve feature extraction topology

et al. 2016

Neural Comput & Applic

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-Dimensional constrained SelfOrganizing Map Preserving the Input Topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example