Spatio-temporal elastic cuboid trajectories for efficient fight recognition using Hough forests

Serrano, Ismael; Deniz, Oscar; Bueno, Gloria; Garcia-Hernando, Guillermo; Kim, Taekyun

doi:10.1007/s00138-017-0894-7

Cited by 20 publications

(8 citation statements)

References 35 publications

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“…The human body parts trajectory can be used to classify more complex actions, for example, to classify various everyday activities (playing piano, push ups, tennis swing, etc.) [ 28 ] or to detect fights [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Kashevnik

Othman

Ryabchikov

et al. 2021

Sensors

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Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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Section: Methodsmentioning

confidence: 99%

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Kashevnik

Othman

Ryabchikov

et al. 2021

Sensors

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“…In addition, the Spatio-Temporal Elastic Cuboid (STEC) Trajectories [6] method for fight detection is proposed. This method is based on the use of blob movements to create trajectories that capture and model the different motions that are specific to a fight.…”

Section: Proposed Methodsmentioning

confidence: 99%

Contributions to the Problem of Fight Detection in Video

Serrano-Gracia

2018

ELCVIA

Self Cite

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While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in camera smartphones. The clear practical applications have led to a surge of interest in developing violence detectors. Correspondence to: < isma 150@hotmail.com/ismael.serrano@uclm.es > Recommended for acceptance by Anjan Dutta and Carles Sánchez

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“…9. It shows the results of CRC classifier [34], HOG and HOF [35], HMM-MIO [36], RecCapsNet & LSTM [37], OU, AS and VS and OU, AS and VS (sum product and RF) [38].…”

Section: Comparison With Other Systemsmentioning

confidence: 99%

Sensors-Based Ambient Assistant Living via E-Monitoring Technology

Hafeez¹,

Ghadi²,

Alarfaj³

et al. 2022

Computers, Materials &Amp; Continua

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Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, waypoint trajectory, Hilbert transform, Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions. These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method. Finally, the Masi entropy classifier is applied for action recognition and classification. Experiments have been performed on three challenging datasets, namely, UTD-MHAD, 50 Salad, and CMU-MMAC. During experimental evaluation, the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1% with UTD-MHAD dataset, 90.6% with 50 Salad dataset, and 89.5% with CMU-MMAC dataset. Hence experimental results validated the proposed system.

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Spatio-temporal elastic cuboid trajectories for efficient fight recognition using Hough forests

Cited by 20 publications

References 35 publications

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Contributions to the Problem of Fight Detection in Video

Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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