Human activity recognition is closely related to human health and is a hot research topic. With the continuous development of AI technology, large models have shown great potential in various training tasks. However, there is limited analysis of human sensor motion data. In this study, we fine-tuned large models on five motion datasets, designed an adaptation layer suitable for time series data to extract action representations fully, and proposed a novel HAR framework. We conducted action recognition experiments on five foundation models and compared them with ten classical algorithm models. The experimental results show that the accuracy of action recognition in small-parameter pre-trained large models can reach 98.8%, indicating significant research potential.

A real-time vision system operating at an outdoor swimming pool is presented in this paper. The system is designed to automatically recognize different swimming activities and to detect occurrence of early drowning incidents. We have named this system the Drowning Early Warning System (DEWS). One key challenge we faced in the problem is the relatively high level of noise in the steps of foreground detection and behavior recognition. Therefore, a set of methods in the fields of background subtraction, denoising, data fusion and blob splitting are proposed, which have been motivated by characteristics of aquatic background and crowded scenario at the pool. In the step to detect an early drowning incident, visual indicators of distress and drowning are incorporated through a set of foreground descriptors. A module comprising data fusion and hidden Markov modeling is developed to learn unique traits of different swimming behaviors, in particular, those early drowning events. The experiment of this work reports realistic on-site evaluations performed. Examples of interesting behaviors, i.e., distress, drowning, treading and numerous swimming styles, are simulated and collected. Experimental results show that we have established a prototype system which is robust and beyond the stage of proof-of-concept.

This paper presents a real-time foreground detection method for monitoring swimming activities at an outdoor swimming pool. Robust performance and high accuracy of detecting objects-of-interest are two central issues of concern. Therefore, in this paper, a considerable amount of attention has been placed on the following aspects: 1) to establish a better method of modeling aquatic background, which exhibitis dynamic characteristics with random spatial movements, and 2) to establish a method of enhancing the visibility of the foreground by removing specular reflection at nighttime. First, the development of a new background modeling method is reported. In the proposed approach, the background is modeled as a composition of homogeneous blob movements. With an implementation of a spatial searching process, the proposed method shows capability in associating and distinguishing movements caused by the background. Hence, this contributes to better performance in foreground detection. On the issue of enhancing the visibility of the foreground, a decision-based filtering scheme is proposed as a preprocessing step. A defined concept term, fluctuation measure, is defined for classifying each pixel to be one of the predefined types. This has allowed suitable spatial or spatiotemporal filters to be applied accordingly for color the compensation step. All of these developments are evaluated by testing live on a busy Olympic-size outdoor public swimming pool. Both qualitative and quantitative evaluations are reported. This provides a comprehensive study of the system.

An adaptive spatio-temporal filtering scheme based on a novel concept of motion frequency is proposed as the preprocessing step to enhance human detection under a noisy aquatic environment. In this framework, each pixel is first classified into one of three categories quantified by its motion frequency, each of which is filtered using an appropriate filtering scheme. For regions affected by glistening reflections and glares, a color compensation filter is specially developed to improve partly hidden human detection and minimize errors due to moving background elements. Additionally, a blob-based verification procedure is introduced to remove the target's shadow. Experimental results demonstrate the effectiveness of the algorithm and its role in enhancing the robustness of an aquatic surveillance system for outdoor swimming pools at nighttime.

Automatically understanding events happening at a site is the ultimate goal of visual surveillance system. We investigate the challenges faced by automated surveillance systems operating in hostile conditions and demonstrate the developed algorithms via a system that detects water crises within highly dynamic aquatic environments. An efficient segmentation algorithm based on robust block-based background modelling and thresholding-with-hysteresis methodology enables swimmers to be reliably detected amid reflections, ripples, splashes and rapid lighting changes. Partial occlusions are resolved using a Markov Random Field framework that enhances the tracking capability of the system. Visual indicators of water crises are identified based on professional knowledge of water crises detection, based on which a set of swimmer descriptors has been defined. Through seamlessly fusing the extracted swimmer descriptors based on a novel functional link network, the system achieves promising results for water crises detection. The developed algorithms have been incorporated into a live system with robust performance for different hostile environments faced by an outdoor swimming pool.

A novel color image descriptor, called color distance histogram (CDH), is proposed in this paper as a fundamental signal feature, readily to be exploited for various color image applications, such as indexing and retrieval, segmentation, and so on. To establish CDH, color image is first represented in CIE L*a*b* color space, followed by using CIE L*a*b*'s uniform color distance metric on computing the distance of each pixel with respect to the reference color. Consequently, CDH accurately reflects the degree of color similarity of individual pixel with respect to the reference color. To demonstrate the use and effectiveness of CDH, it is further extended into a set of CDHs, called dominant color profile (DCP), for color image segmentation. Experimental results clearly indicate that the proposed CDH-based or DCP-based segmentation method yields superior segmentation to other thresholding and clustering methods, in terms of accuracy, robustness, efficiency and computational complexity.