Probabilistic Posture Classification for Human-Behavior Analysis

Cucchiara, Rita; Grana, Costantino; Prati, Andrea; Vezzani, Roberto

doi:10.1109/tsmca.2004.838501

Cited by 157 publications

(76 citation statements)

References 22 publications

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“…These activities can be recognized either by a camera sensor running a video analysis algorithm [3] or by an accelerometer sensor analyzing the acceleration patterns [6]. We installed the camera sensor in our department and configured it to upload arising labels to the tuple space.…”

Section: Implemented Case Studymentioning

confidence: 99%

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Unsupervised learning in body-area networks

Bicocchi

Lasagni

Mamei

et al. 2010

Proceedings of the Fifth International Conference on Body Area Networks

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Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.

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Section: Implemented Case Studymentioning

confidence: 99%

“…apparent moving objects typically associated with the "aura" that an object that begins moving leaves behind itself. Further details can be found in [3].…”

Section: Camera Sensormentioning

confidence: 99%

Unsupervised learning in body-area networks

Bicocchi

Lasagni

Mamei

et al. 2010

Proceedings of the Fifth International Conference on Body Area Networks

View full text Add to dashboard Cite

show abstract

“…Usually this means that some low-level features of the body segment of the image, such as projection histograms [6,4,11] or contour based shape descriptors [11], are computed in order to achieve this classification. Otherwise a template is obtained to represent a single class of postures, and then the image is compared with the whole set of templates to find the best match, for example using Chamfer matching [9].…”

Section: Related Workmentioning

confidence: 99%

Human Posture Tracking and Classification Through Stereo Vision

Pellegrini

Iocchi²

2006

Proceedings of the First International Conference on Computer Vision Theory and Applications

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The ability of detecting human postures is very relevant for applications related to the analysis of human behaviors. Techniques for posture detection and classification can be thus very relevant in several fields, like ambient intelligence, surveillance, elderly care, human-machine interaction. This problem has been studied in recent years in the Computer Vision community, but proposed solutions still suffer from some limitations that are due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.).In this article we present a system for posture tracking and classification based on a stereo vision sensor that provides both a robust way to segment and track people in the scene and 3D information about tracked people. The proposed method is based on matching 3D data with a 3D human body model. Relevant points in the model are then tracked over time with temporal filters and a classification method based on Hidden Markov Models is used to recognize principal postures. Experimental results show the effectiveness of the system in determining human postures with different orientations of the people with respect to the stereo sensor, in presence of partial occlusions and under different environmental conditions.

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“…In a similar manner, but with lower complexity, Niu et al [6] use only the object position and velocity to design curves which describe the motion. The use of people gait, represented through an histogram, was used to classify non-conventional situations inside a house [7]. Classification is carried out through a regular histogram.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Human Movements in Video Scenes

Hochuli

Oliveira

Britto

et al. 2007

Advances in Image and Video Technology

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Abstract.A novel approach for the detection and classification of human movements in videos scenes is presented in this paper. It consists in detecting, segmenting and tracking foreground objects in video scenes to further classify their movements as conventional or non-conventional. From each tracked object in the scene, features such as position, speed, changes in direction and temporal consistency of the bounding box dimension are extracted. These features make up feature vectors that are stored together with labels that categorize the movement and which are assigned by human supervisors. At the classification step, an instancebased learning algorithm is used to classify the object movement as conventional or non-conventional. For this aim, feature vectors computed from objects in motion are matched against reference feature vectors previously labeled. Experimental results on video clips from two different databases (Parking Lot and CAVIAR) have shown that the proposed approach is able to detect non-conventional human movements in video scenes with accuracies between 77% and 82%.

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Probabilistic Posture Classification for Human-Behavior Analysis

Cited by 157 publications

References 22 publications

Unsupervised learning in body-area networks

Unsupervised learning in body-area networks

Human Posture Tracking and Classification Through Stereo Vision

Detection and Classification of Human Movements in Video Scenes

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