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Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration. [ABSTRACT FROM AUTHOR]

In this paper, a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3D object. The descriptor is based on taking "snapshots" of the surface over each point using a virtual camera oriented perpendicularly to the surface. This representation has the advantage of imposing minimal loss of information be robust to self-occlusions and also be very efficient to compute. Then, we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap. Index Terms--Surface matching, object recognition, partially overlapping surfaces.

A complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data based on a low-cost 3-D sensor capable of real-time acquisition of 3-D and color images is developed. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images and the experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented. Index Terms - Echocardiography, tracking.

An object-based stereo image coding algorithm is proposed. The algorithm relies on modeling of the object structure using 3-D wire-frame models and motion estimation using globally rigid and locally deformable motion models. Algorithms for the estimation of motion and structure parameters from stereo images are described. Motion parameters are used to construct predicted images by mapping the image texture on the object surface. Coding of object parameters, appearing background regions, and prediction errors are investigated, and experimental results with standard stereo image sequences depicting general scenes are presented. The proposed algorithm is seen to be very efficient for applications such as stereoscopic video transmission. Furthermore, it is especially well suited to advanced applications such as generation and transmission of intermediate views for multiview receiver systems and applications in which objectwise editing of the bit stream is required. The latter include video production using preanalyzed scenes and virtual reality applications. Index Terms - 3-D models, object-based coding, stereo sequences.

In this paper, a motion estimation algorithm is presented, based on spatiotemporal warping of an image sequence. The motion field function is modeled by isoparametric cubic finite elements, depending on a set of elementary displacement vectors. A model refinement scheme is proposed for the adaptively higher bit allocation to motion information in locations with high-motion activity. Index Terms - Motion estimation, spatiotemporal warping, very low bit-rate coding.