Human Action Recognition: Contour-Based and Silhouette-Based Approaches

Al-Ali, Salim; Milanova, Mariofanna; Al‐Rizzo, Hussain M.; Fox, Victoria L.

doi:10.1007/978-3-319-11430-9_2

Cited by 16 publications

(10 citation statements)

References 41 publications

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“…Large numbers of papers are composed for Human activity recognition. One sort of research pursues face profile [10], gait [11], and silhouette [12]. Gait Image is utilized to depict human strolling properties.…”

Section: Literature Reviewmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named "L4-Branched-ActionNet". The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in terms of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work's soundness.

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Section: Literature Reviewmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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“…Marín-Jiménez et al [9] proposed a multiscale descriptor for human action recognition obtained through pyramids of optical flow histograms and tested their technique over standard datasets. Al-Ali et al [10] explained contour and silhouette based approaches for human action classification in their book chapter. While investigating the contour-based techniques, they put the emphasis on four features i.e., chord-length, Cartesian coordinate, centroid-distance, and Fourier descriptors' features.…”

Section: Features Based Action Recognition From 2d Videosmentioning

confidence: 99%

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

et al. 2020

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Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.

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“…Classical approaches model actions from geometric primitives are computed from temporal silhouettes [9] to local representations that deal with occlusion problems [10]. These approaches are generally limited to describing global or appearance-based changes along time, losing dynamic representation that could be determinant to differentiate among comparable actions.…”

Section: Introductionmentioning

confidence: 99%

Local Trajectory Occurrence Patterns for Partial Action and Gesture Recognition

Garzón¹,

Martínez²

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Action and gesture recognition is essential in computer vision because of their multiple and potential applications. Nowadays, in the literature, dramatic advances have been reported regarding recognizing gestures and actions under uncontrolled scenarios with significant appearance and motion variations. Nevertheless, much of these approaches still require manual segmentation of temporal action boundaries and complete processing of whole sequences to obtain a prediction. This work introduces a novel motion description that can recognize actions and gestures over partial sequences. The approach starts by representing video sequences as a set of keypoint trajectories. Such trajectories are then hierarchically represented from a local and regional perspective, following a statistical counting process. Firstly, each trajectory is defined as a binary occurrence pattern that allows for standing out critical motions by neighborhood densities from a local perspective. Such occurrence patterns are involved in a regional bag-of-words representation of actions. Both representations could be obtained for any interval inside the video, achieving a partial recognition of motion, and regional representation is mapped to a support vector machine to obtain a prediction. The proposed approach was evaluated on academic action recognition datasets and a large gesture dataset used for sign recognition. Regarding partial video sequence recognition, the proposed approach achieves an accuracy rate of 63% using only 20% of frames. The proposed strategy achieved a very compact description, with only 400 scalar values, which ideal for online applications.

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Human Action Recognition: Contour-Based and Silhouette-Based Approaches

Cited by 16 publications

References 41 publications

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

Local Trajectory Occurrence Patterns for Partial Action and Gesture Recognition

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