Review of Human Motion Detection based on Background Subtraction Techniques
18 Jul 2015-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 122, Iss: 13, pp 1-5
TL;DR: This paper provides a review of the human motion detection methods focusing on background subtraction technique and concludes that current methods for detecting objects in motion within videos from static cameras are inadequate.
Abstract: For the majority of computer vision applications, the ability to identify and detect objects in motion has become a crucial necessity. Background subtraction, also referred to as foreground detection is an innovation used with image processing and computer vision fields when trying to detect an object in motion within videos from static cameras. This is done by deducting the present image from the image in the background or background module. There has been comprehensive research done in this field as an effort to precisely obtain the region for the use of further processing (e.g. object recognition). This paper provides a review of the human motion detection methods focusing on background subtraction technique.
Citations
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TL;DR: The proposed forest fire detection algorithm consists of background subtraction applied to movement containing region detection, and temporal variation is employed to differentiate between fire and fire-color objects.
Abstract: Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.
37 citations
Cites background from "Review of Human Motion Detection ba..."
...A pixel located at (x, y) is supposed to be moving if the following condition is satisfied [8]....
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TL;DR: This is the first study based on a novel combination of 3D-convolutional neural networks fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition.
Abstract: Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition. The proposed architecture is constructed combining dense optical flow approach and auxiliary movement information in video datasets using deep learning methodologies. To the best of our knowledge, this is the first study based on a novel combination of 3D-convolutional neural networks (3D-CNNs) fed by optical flow and long short-term memory networks (LSTM) fed by auxiliary information over video frames for the purpose of human activity recognition. The contributions of this paper are sixfold. First, a 3D-CNN, also called multiple frames is employed to determine the motion vectors. With the same purpose, the 3D-CNN is secondly used for dense optical flow, which is the distribution of apparent velocities of movement in captured imagery data in video frames. Third, the LSTM is employed as auxiliary information in video to recognize hand-tracking and objects. Fourth, the support vector machine algorithm is utilized for the task of classification of videos. Fifth, a wide range of comparative experiments are conducted on two newly generated chess datasets, namely the magnetic wall chess board video dataset (MCDS), and standard chess board video dataset (CDS) to demonstrate the contributions of the proposed study. Finally, the experimental results reveal that the proposed hybrid deep model exhibits remarkable performance compared to the state-of-the-art studies.
27 citations
Cites background from "Review of Human Motion Detection ba..."
...motion; and human action and object detection, in which the system is able to localize human activity in an image [3]....
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01 Sep 2018
TL;DR: Thanks to this work, students will not have to look for a place to work when the library is crowded and will not bother other working students and it is believed that this project will serve all students.
Abstract: In this study, a real-time system which counts the number of people with the help of a camera was demonstrated. The system can send the number of people to a mobile application via Internet of Things (IoT) and monitor simultaneously. This work is carried out in the main library of Inonu University. Background subtraction method was used to recognize moving humans on the visual field of the camera. According to motion information of humans, a counter was used to count the number of people in the saloon by determining whether going inside or outside. The counter will inform the users about what percentage of the saloon is empty. Matlab and Thingspeak combination help to send counter information to internet environment. A mobile application was used to track the counter information from Android and iOS smartphones. The results were presented in Matlab environment and mobile application simultaneously. Thanks to this work, students will not have to look for a place to work when the library is crowded and will not bother other working students. It is believed that this project will serve all students.
12 citations
Cites background or methods from "Review of Human Motion Detection ba..."
...There are some methods to get the initial image in literature [15]....
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...In our real system, background subtraction method always includes some noises which can be caused by the environment or light amount [15]....
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...T is threshold rate determined manually by looking experimental results [15,19]....
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...This stage has a great importance over the process because It is quite difficult to understand human motion from noisy image [15,16,19]....
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...If there is no moving object in the current frame, we update the background image with its new values [15,17,18]....
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01 Aug 2018
TL;DR: The experimental results show that the combination of the three-frame difference method and the background difference method can effectively remove the noise and ghosting, which avoids inaccurate object extraction, caused by the background different method and avoids the incomplete moving object of the inter- frame difference method.
Abstract: Aiming at the problem of ghosting detected by two-frame different method and the defects existed in the independent detection by inter-frame difference method and background difference method, three-frame difference method is proposed in this paper. That is, the different result between frame and frame can be calculated. The outline of the moving target is roughly marked to solve the problem of ghosting. At the same time, we combine the three frame difference method and the background difference method to get the algorithm of this paper. The algorithm uses a mixed Gaussian method to establish a background model and modify the variance update so that the background model fits with the real background and performs morphological processing to extract the moving target. The experimental results show that the combination of the three-frame difference method and the background difference method can effectively remove the noise and ghosting, which avoids inaccurate object extraction, caused by the background difference method and avoids the incomplete moving object of the inter-frame difference method.
7 citations
Cites methods from "Review of Human Motion Detection ba..."
...In reference [4], the statistical averaging method is used to model the background difference method, and the interference of other noises to the background can be reduced by calculating the average value....
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01 Sep 2020
TL;DR: A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster human tracking algorithm, is proposed, which can be achieved using a single, low cost day/night NIR camera.
Abstract: This paper presents the study and the evaluation of GPS/GNSS techniques combined with advanced image processing algorithms for the precise detection, positioning and tracking of distressed humans. In particular, the issue of human detection on both terrestrial and marine environments, as the human silhouette in a marine environment may differ substantially from a land one, is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster human tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates, under several illumination and background conditions, can be achieved using a single, low cost day/night NIR camera. It is mounted onboard a fully autonomous UAV for Search and Rescue (SAR) missions. Moreover, the collection of a novel dataset, suitable for training the computer vision algorithms is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.
4 citations
References
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01 Jan 2013
TL;DR: Wagholi et al. as mentioned in this paper presented a new algorithm for detecting moving objects from a static background scene to detect moving object based on background subtraction and morphological filtering.
Abstract: * ME (Electronics and Telecommunication), GHRCEM Wagholi, Pune ** Electronics and Telecommunication, GHRCEM Wagholi, Pune Abstract- Recent research in computer vision has increasingly focused on building systems for observing humans and understanding their look, activities, and behavior providing advanced interfaces for interacting with humans, and creating sensible models of humans for various purposes In order for any of these systems to function, they require methods for detecting people from a given input image or a video Visual analysis of human motion is currently one of the most active research topics in computer vision In which the moving human body detection is the most important part of the human body motion analysis, the purpose is to detect the moving human body from the background image in video sequences, and for the follow-up treatment such as the target classification, the human body tracking and behavior understanding, its effective detection plays a very important role Human motion analysis concerns the detection, tracking and recognition of people behaviors, from image sequences involving humansAccording to the result of moving object detection research on video sequences This paper presents a new algorithm for detecting moving objects from a static background scene to detect moving object based on background subtraction We set up a reliable background updating model based on statistical After that, morphological filtering is initiated to remove the noise and solve the background interruption difficulty At last, contour projection analysis is combined with the shape analysis to remove the effect of shadow; the moving human bodies are accurately and reliably detected The experiment results show that the proposed method runs rapidly, exactly and fits for the concurrent detection
48 citations
01 Jan 2014
TL;DR: A new algorithm for detecting moving objects from a static background scene to detect moving object based on background subtraction is presented, set up a reliable background updating model based on statistical and fits for the concurrent detection.
Abstract: Consider all the features of subset information in video streaming there is a tremendous processes with real time applications. In this paper we introduce and develop a new video surveillance system. Using this technique we detect human normal and exponential behaviors in realistic format, and also we categories data event generation of human tracking in real time applications. In this technique we apply differencing, threshold segmentation, morphological operations and object tracking. The experimental result show efficient human tracking in video streaming operations.
39 citations
"Review of Human Motion Detection ba..." refers background in this paper
...These include birds, vehicles, swaying trees, and floating clouds [9]....
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01 Dec 2006
TL;DR: The detection method of a moving object by mapping is introduced, which converts the motion of a stationary environment object into a linear signal trajectory and calculates the one-dimensional optical flow by using pixels, which belong to the moving object, to eliminate the apparent motion of the stationary environmentobject.
Abstract: The optical flow is a useful tool for the tracking of a moving object. Estimation of the optical flow based on the gradient method is an ill-posed problem. In order to avoid this ill-posed problem, we proposed a tracking method using a one-dimensional optical flow, which is calculated on a straight line (called the calculation axis) spanning several directions. However, the motion of the observer was not considered. In this paper, we propose object tracking by a one-dimensional optical flow under a rotating observer. The apparent motion of a stationary environment object should be eliminated for calculating the one-dimensional optical flow. Hence, we introduce the detection method of a moving object by mapping, which converts the motion of a stationary environment object into a linear signal trajectory. We calculate the one-dimensional optical flow by using pixels, which belong to the moving object, to eliminate the apparent motion of the stationary environment object. In order to verify the efficacy of the proposed method, simulation is performed using synthesized images. The proposed method successfully tracks the moving object when the observer rotates at a constant angular velocity
31 citations
"Review of Human Motion Detection ba..." refers background or methods in this paper
...Hence, its only usage is within situations in which the background can be predicted or is known [4]....
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...The optical flow process is put in place in order to compute the field of the image optical flow and also to administer clustering processing in keeping with the distribution of the optical flow nature of the image [4]....
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03 Apr 2013
TL;DR: A new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role and is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable.
Abstract: In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Among all existing algorithms it was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.
24 citations
"Review of Human Motion Detection ba..." refers background or methods in this paper
...In the event that the pixel-value for a pixel given surpasses the threshold (Th) value, that specific pixel is treated as though it is a part of the foreground [3]....
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...Of the various previously stated methods, the most common method is used for the initialization of a background image [3]....
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01 Jan 2014
TL;DR: This work proposes a general-purpose method which combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts) and shadows acquired in the processing of the previous frames to supply an object-based selective update.
Abstract: Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work proposes a general-purpose method which combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts) and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits gray color information for both background subtraction to improve object segmentation. The approach proves fast, flexible and precise in terms of pixel accuracy. The implementation of the background subtraction algorithm is done in two domains code is written in Matlab, then using Simulink blocks sets.
14 citations