scholarly journals Face Recognition and Summarization for Surveillance Video Sequences

2020 ◽  
Author(s):  
Somaya Maadeed ◽  
Noor Almaadeed ◽  
Omar Elharrouss
Keyword(s):  
Face Recognition ◽  
Video Summarization ◽  
Human Action Recognition ◽  
Human Action ◽  
Surveillance Video ◽  
Specific Sequence ◽  
Action Detection ◽  
Detection And Tracking ◽  
Single Person ◽  
Human Action Detection

Face recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the temporal difference map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training.

scholarly journals Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Dengshan Li ◽  
Rujing Wang ◽  
Peng Chen ◽  
Chengjun Xie ◽  
Qiong Zhou ◽  
...  
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Detection Methods ◽  
Video Object ◽  
Action Detection ◽  
Video Frames ◽  
Video Detection ◽  
Human Action Detection

Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detection of human actions. Usually, video detection is more challenging than image detection, since video frames are often more blurry than images. Moreover, video detection often has other difficulties, such as video defocus, motion blur, part occlusion, etc. Nowadays, the video detection technology is able to implement real-time detection, or high-accurate detection of blurry video frames. In this paper, various video object and human action detection approaches are reviewed and discussed, many of them have performed state-of-the-art results. We mainly review and discuss the classic video detection methods with supervised learning. In addition, the frequently-used video object detection and human action recognition datasets are reviewed. Finally, a summarization of the video detection is represented, e.g., the video object and human action detection methods could be classified into frame-by-frame (frame-based) detection, extracting-key-frame detection and using-temporal-information detection; the methods of utilizing temporal information of adjacent video frames are mainly the optical flow method, Long Short-Term Memory and convolution among adjacent frames.

scholarly journals Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

2017 ◽  
Author(s):  
Mohammadamin Barekatain ◽  
Miquel Marti ◽  
Hsueh-Fu Shih ◽  
Samuel Murray ◽  
Kotaro Nakayama ◽  
...  
Keyword(s):  
Human Action ◽  
Action Detection ◽  
Human Action Detection ◽  
Aerial View

Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments

2015 ◽  
Vol 6 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Dianting Liu ◽  
Yilin Yan ◽  
Mei-Ling Shyu ◽  
Guiru Zhao ◽  
Min Chen
Keyword(s):  
Action Recognition ◽  
Hamming Distance ◽  
Gaussian Mixture ◽  
Human Action ◽  
Surveillance Systems ◽  
Camera Motion ◽  
Action Detection ◽  
Public Data ◽  
Human Action Detection ◽  
Detection And Recognition

Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.

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