Human Activity Tracking Research Articles

Handling real-world context awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method.

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.

Human activity recognition based on kinematic features

AbstractThis paper proposes a new approach to recognize human actions in 2D sequences, based on real‐time visual tracking and simple feature extraction of human activities in video sequences. The proposed method emphasizes the simplicity of the strategies used, in an attempt to describe human actions as precisely as other more sophisticated (and more computationally demanding) methods in the literature. Specifically, we propose three complementary modules for the following: (a) tracking; (b) feature extraction; and (c) action recognition. The first module is based on the hybridization of a particle filter and a local search procedure and makes use of a reduced integral image to speed up the weight computation. The feature extraction module characterizes the silhouette of the tracked person by dividing it into rectangular boxes. Then, the system computes statistics on the evolution of these rectangular boxes over time. Finally, the action recognition module passes these statistics to a support vector machine to classify the actions. Experimental results show that the proposed method works in real‐time, and its performance is competitive against other state‐of‐the‐art methods.

A Tempo-Topographical Model Inference of a Camera Network for Video Surveillance

A method is proposed to automatically construct a tempo-topographical model of a wireless IP-multi-camera network using some observations of known moving targets. The method is entirely unsupervised with no calibration of the cameras. This enables to extend the activity analysis across the entire camera network, constructing transition model between non-overlapping camera views, and also enabling the tracking across the blind-regions of the network. Based only on camera spatial and temporal information, this approach eliminates redundancy in the paths linking any pair of cameras by using the weighted graph technique to each node. The experimental results on real-time video streams show the feasibility of our system and its effectiveness in human activity tracking.

1A2-S03 Tracking of Human Activities in Smart Home Based on Bayesian Networks(Integrating Ambient Intelligence(2))

1A2-S03 Tracking of Human Activities in Smart Home Based on Bayesian Networks(Integrating Ambient Intelligence(2))

Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation

With decreasing costs of high-quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. We propose a methodology for detecting this event in real time by applying a novel image projection that reduces the dimensionality of the data and, thus, reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require 2-D data. We evaluate the performance of our algorithm using the i-LIDS vehicle detection challenge datasets as well as videos we have taken ourselves. These videos test the algorithm in a variety of outdoor conditions, including nighttime video and instances of sudden changes in weather.

An integrated surveillance system—human tracking and view synthesis using multiple omni-directional vision sensors

Accurate and efficient monitoring of dynamically changing environments is one of the most important requirements for visual surveillance systems. This paper describes the development of an integrated system for this monitoring purpose. The system consists of multiple omni-directional vision sensors and was developed to address two specific surveillance tasks: (1) robust tracking and profiling of human activities; (2) dynamic synthesis of virtual views for observing the environment from arbitrary vantage points.