A survey on Human action recognition from videos

Dhamsania, Chandni; Ratanpara, Tushar V.

doi:10.1109/get.2016.7916717

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…An ideal recognition model relies on effective learning of action features. In recent years, many different techniques are applied to extract and represent both short-term and long-term spatiotemporal features [31]. For human interaction recognition, the Harris corner points and the histogram [32], and a compact and discriminative video encoding method [33] were proposed to extract features.…”

Section: Related Work a Conventional Action Recognitionmentioning

confidence: 99%

A Discriminative Deep Model With Feature Fusion and Temporal Attention for Human Action Recognition

Gao

Yang

et al. 2020

IEEE Access

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Activity recognition which aims to accurately distinguish human actions in complex environments plays a key role in human-robot/computer interaction. However, long-lasting and similar actions will cause poor feature sequence extraction and thus lead to a reduction of the recognition accuracy. We propose a novel discriminative deep model (D3D-LSTM) based on 3D-CNN and LSTM for both single-target and interaction action recognition to improve the spatiotemporal processing performance. Our models have several notable properties: 1) A real-time feature fusion method is used to obtain a more representative feature sequence through composition of local mixtures for enhancing the performance of discriminating similar actions; 2) We introduce an improved attention mechanism that focuses on each frame individually by assigning different weights in real-time; 3) An alternating optimization strategy is proposed for our model to obtain parameters with the best performance. Because the proposed D3D-LSTM model is efficient enough to be used as a detector that recognizes various activities, a Real-set database is collected to evaluate action recognition in complex real-world scenarios. For long-term relations, we update the present memory state via the weight-controlled attention module that enables the memory cell to store better long-term features. The densely connected bimodal modal makes local perceptrons of 3D-Conv motion-aware and stores better short-term features. The proposed D3D-LSTM model has been evaluated through a series of experiments on the Real-set and open-source datasets, i.e. SBU-Kinect and MSR-action-3D. Experimental results show that the proposed D3D-LSTM model achieves new state-of-the-art results, including pushing the average rate of the SBU-Kinect to 92.40% and the average rate of the MSR-action-3D to 95.40%.

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Section: Related Work a Conventional Action Recognitionmentioning

confidence: 99%

A Discriminative Deep Model With Feature Fusion and Temporal Attention for Human Action Recognition

Gao

Yang

et al. 2020

IEEE Access

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“…Person, one of the most concerned parts of the data collected by city cameras, is getting a lot of attention in the field of computer vision. So there are various personrelated researches, such as detection [280], attributes [11], re-identification, tracking [283], action recognition [284], semantic segmentation [282], and face recognition [281]. Person re-identification is closely connected with other person-related researches.…”

Section: A Development Coursementioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference. INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.

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“…Dhamsania and Ratanpara implied that action recognition can be estimated by computing the PCA–HOG descriptor of the tracking region in frames. The experiments were performed for two games: hockey and soccer [ 26 ]. However, those methods indicated that the computational time should be considered carefully.…”

Section: Related Workmentioning

confidence: 99%

Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition

Jiao

Wang

Deng

2017

Sensors

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In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA) to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG–PCA (HOGP), is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature.

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A survey on Human action recognition from videos

Cited by 26 publications

References 11 publications

A Discriminative Deep Model With Feature Fusion and Temporal Attention for Human Action Recognition

A Discriminative Deep Model With Feature Fusion and Temporal Attention for Human Action Recognition

A Comprehensive Overview of Person Re-Identification Approaches

Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition

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