Low Resolution Camera for Human Detection and Tracking

— Object detection is a crucial part in today's video surveillance systems. Many methods have evolved over the years that include Background Subtraction at the pinnacle. Background subtraction is a technique in which the video is segmented in multiple frames. A base frame called as " Background " is used to subtract another frame from it to detect " Foreground ". Motion–based and shape-based algorithms boost the Background subtraction method. The multiple objects detection technique used in surveillance system uses Support Vector Machine (SVM) to detect and classify the different objects. In this project, study proposes a novel object detection and its classification using Support Vector Machine (SVM) which is used to differentiate objects according to the set of points on the objects. The algorithm then aims at the classification of these key-points, namely at discriminating between the points which belongs to objects and all the others, by means of a Support Vector Machine (SVM) classifier. At the end of the procedure, the objects present inside the scene are identified by analyzing at the key-points previously classified as specific object points. It begins with a feature extraction process from which a set of consistent key-points is identified. Being able to identify specific objects or a particular class of objects in an image can provide several advantages and can open the door to the development of various interesting applications.

Detecting moving objects in video sequences is very important in visual surveillance. This describes a method for accurately tracking persons in indoor surveillance video stream obtained from a static camera with difficult scene properties including illumination changes and solves the major occlusion problem. Simple image processing with frame differentiation method is applied to identify multiple human motions. Firstly, a crowd is segmented by framedifference technique, followed by morphological processing and region growing. Detecting and tracking multiple moving people in a complex environment with indoor surveillance video stream obtained from a static camera. The background subtraction method is to use the difference method of the current image and background image to detect moving objects, with simple algorithm, but very sensitive to the changes in the external environment. The effectiveness of the proposed method is demonstrated with experiments in an indoor environment.

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

In this paper, we present a real time robust human detection and tracking system for video surveillance which can be used in varying environments. This system consists of human detection, human tracking and false object detection. The human detection utilizes the background subtraction to segment the blob and use codebook to classify human being from other objects. The optimal design algorithm of the codebook is proposed. The tracking is performed at two levels: human classification and individual tracking .The color histogram of human body is used as the appearance model to track individuals. In order to reduce the false alarm, the algorithms of the false object detection are also provided.

Image and Vision Computing, 2009

In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short-and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al.

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification, fall detection for elderly people, etc. The first step of the detection process is to detect an object which is in motion. Object detection could be performed by using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being by using shape-based, texturebased or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

International Journal of Science Technology & Engineering

Extracting high level features is an important field in video indexing and retrieving. Identifying the presence of human in video is one of these high level features, which facilitate the understanding of other aspects concerning people or the interactions between people. Our work proposes a method for identifying the presence of human in videos. The proposed algorithm detects the human face based on the colour and motion information extracted from frames over wide range of variations in lightning conditions, skin colour races, backgrounds and faces' sizes and orientations. Experimental results demonstrate the successfulness of the algorithm used and its capability in detecting faces under different challenges. The proposed work is crucial in lots of applications whose concern is mainly human activities and can be a basic step in such activities. So, for that an algorithm has been proposed to detect the presence of human in video sequence. The main technique used in building the proposed algorithm is motion detection technique. A series of stages were implemented in a certain order to promise maximizing the detection of Human Motion and eliminating the other objects (noise). The proposed algorithm detects Human Motion among Non-human objects.

IRJET, 2021

Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos, testing several different motions coding schemes and showing empirically that orientated histograms give the best overall performance. Use of human modelling to recognize and monitor human activity in the scene such as human walking, running etc is tracked. In addition to videos, detection from a static image can also be carried out by providing image as an input instead of a live feed from a CCTV footage respectively. Human detection in videos (i.e., series of images) plays an important role in various real-life applications (e.g., visual surveillance and automated driver assistance). The task of human detection in a series of images is challenging due to various reasons. One of these reasons is the variation of human size in the video frame. This results from changing the altitude of the platform that the camera is attached to during the task. Accuracy and short training time are the two important factors that should be taken into consideration to get a robust human, nonhuman classification system. The current Covid-19 Pandemic has altogether increased the need of such sustainable system that is Real time human detection to avoid any mishap and limit the spread of the virus, with the help of detected persons required actions can be taken by the concerned authorities respectively.

2000

In environments where a camera is installed on a freely moving platform, e.g. a vehicle or a robot, object detection and tracking becomes much more difficult. In this paper, we presents a real time system for human detection, track- ing, and verification in such challenging environments. To deliver a robust performance, the system integrates several computer vision algorithms to perform

2013

In this paper we are proposing a fast and efficient algorithm to track humans in a indoor surveillance video. Here we have used hog features and correlation based method to track humans in a surveillance video. Strips on the borders of a frame is analyzed to know the entry of new objects in to the frame. This algorithm can applied to real time systems due to the low time complexity of the system.

Fourth IEEE International Conference on Computer Vision Systems (ICVS'06), 2006

In environments where a camera is installed on a freely moving platform, e.g. a vehicle or a robot, object detection and tracking becomes much more difficult. In this paper, we presents a real time system for human detection, tracking, and verification in such challenging environments. To deliver a robust performance, the system integrates several computer vision algorithms to perform its function: a human detection algorithm, an object tracking algorithm, and a motion analysis algorithm. To utilize the available computing resources to the maximum possible extent, each of the system components is designed to work in a separate thread that communicates with the other threads through shared data structures. The focus of this paper is more on the implementation issues than on the algorithmic issues of the system. Object oriented design was adopted to abstract algorithmic details away from the system structure.