Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

No abstract

No abstract

No abstract

No abstract

Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre-trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.

No abstract

This paper focuses on the minimization of the time of the dominant straight line detection in an image using Hough Transform algorithm. The idea is a mixture between two domains, namely image processing and multi-agent systems. The importance of this work comes from the relying of image processing techniques on hardware accelerations. This paper demonstrates how the distribution of a purely sequential processing on a set of agents leads to an improvement from time execution point of view. The purpose is to reduce the execution time of Hough transform technique through the distribution of the algorithm on a set of reactive agents. This may allow the exploitation of a parallel or distributed environment. The main idea is based on the division of similar repeated processing with different parameters on several agents. It is a SIMD-like architecture according to Flynn classification. The obtained results are promising in the way that the execution time is at least divided by 4 comparatively to the use of the algorithm in its sequential form.