Detection and Tracking of Humans and Faces (original) (raw)
Related papers
2011
Object Tracking is an important task in video processing because of its various applications like visual surveillance, human activity monitoring and recognition, traffic flow management etc. Multiple object detection and tracking in outdoor environment is a challenging task because of the problems raised by poor lighting conditions, occlusion and clutter. This paper proposes a noble technique for detecting and tracking the multiple humans in a video. A classifier is trained for object detection using haarlike features from the training image set. The human objects are detected with the help of this trained detector and are tracked with the help of a particle filter. The experimental results show that the propose technique can detect and track the multiple humans in a video adequately fast in the presence poor lighting conditions, clutter and partial occlusion and the technique can handle varying number of human objects in the video at various points of time.
Detecting and tracking human faces in videos
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000
A method for detecting and tracking human faces in color videos is presented. The method first uses a chroma chart with information about skin colors of various races to determine regions of skin color in the first frame of a video. A new chroma chart is computed for each region, which more precisely represents the color contents of that region. Chroma charts for different regions that are similar are combined, while those that are considerably different are kept separate. Model facial patterns are then used to detect faces within the skin regions. Once a face is detected, the particular pattern and color of the face are used to track the face. Regions where facial patterns are not detected are expected to correspond to exposed parts of the body or of the background and are ignored. The proposed method can track faces with a high degree of accuracy once they are identified.
Tracking people with probabilistic appearance models
2002
This paper describes a real-time computer vision system for tracking people in monocular video sequences. The system tracks people as they move through the camera's field of view, by a combination of background subtraction and the learning of appearance models. The appearance models allow objects to be tracked through occlusions using a probabilistic pixel reclassification algorithm. The system is evaluated on the three test sequences of the PETS 2002 dataset, for which tracking results and processing time requirements are presented.
An Appearance-based Approach for Consistent Tracking of Humans in Surveillance Video
A computer vision system for tracking multiple people in relatively unconstrained environments is described. For the purpose of efficiently tracking multiple people in the presence of occlusions, we propose: (i) to combine blob matching with particle filtering, and (ii) to augment these tracking algorithms with a novel colour appearance model. The proposed system efficiently counteracts the shortcomings of the two algorithms by switching from one to the other during occlusions. Results on public datasets as well as real surveillance videos from a metropolitan railway station demonstrate the efficacy of the proposed system.
Multiple appearance models for face tracking in surveillance videos
2007
Face tracking is a key component for automated video surveillance systems. It supports and enhances tasks such as face recognition and video indexing. Face tracking in surveillance scenarios is a challenging problem due to ambient illumination variations, face pose changes, occlusions, and background clutter. We present an algorithm for tracking faces in surveillance video based on a particle filter mechanism using multiple appearance models for robust representation of the face. We propose color based appearance model complemented by an edge based appearance model using the Difference of Gaussian (DOG) filters. We demonstrate that combined appearance models are more robust in handling the face and scene variations than a single appearance model. For example, color template appearance model is better in handling pose variations but they deteriorate against illumination variations. Similarly, an edge based model is robust in handling illumination variations but they fail in handling substantial pose changes. Hence, a combined model is more robust in handling pose and illumination changes than either one of them by itself. We show how the algorithm performs on a real surveillance scenario where the face undergoes various pose and illumination changes. The algorithm runs in real-time at 20 fps on a standard 3.0 GHz desktop PC.
A Robust Face Tracking Method by Employing Color-based Particle Filter
Human or face tracking in a diverse environment is very important for various applications in computer vision, especially for video surveillance. Usually, a color cue offers many advantages over motion or geometric information which cannot robustly handle partial occlusion, rotation, scale and resolution changes. In this paper, we present a robust face tracking system by employing a color-based particle filter. The face detection technique is realized based on a Haar-like features algorithm. Here we exploit skin color cues for face-tracking, and it also proposes a body-part particle distribution system. This system is robust against occlusion by human or others and it can perform the tracking in real-time. We conducted experiments in both indoor and outdoor environments, with either a single or multiple persons in a view. Based on the color-based and body-part particle filter, we tracked a person's face satisfactorily by a developed simple robot system.
Automatic Detection of Human in Video and Human Tracking
International Journal of Engineering Research and
Automatic Human detection and tracking is a vital part of video surveillance. Many human detection and human tracking algorithms have been discussed in literature survey. Authors in this paper have attempted to identify the human in clattered environment, identify human body (head, body and leg, track the human in the video based on RGB colour model and also detect collision between multiple human.
Video Object-Tracking Using Particle Filtering and Feature Fusion
Advances in Electrical Control and Signal Systems, 2020
In this paper, a novel video tracking scheme is proposed using the notion of particle filtering. For each pixel of the frame, two features namely Local Binary Pattern (LBP) and the RGB are fused to generate a new feature. Fusion is carried out in the probabilistic framework and the fusion coefficients are determined based on trial and error. Particle filter based modeling is used to track the object in the feature plane. The proposed scheme has been tested on different frames of different benchmarked data sets and the performance of the proposed scheme is found to be superior than the existing method.
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology, 2013
Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models.
Capturing People in Surveillance Video
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
This paper presents reliable techniques for detecting, tracking, and storing keyframes of people in surveillance video. The first component of our system is a novel face detector algorithm, which is based on first learning local adaptive features for each training image, and then using Adaboost learning to select the most general features for detection. This method provides a powerful mechanism for combining multiple features, allowing faster training time and better detection rates. The second component is a face tracking algorithm that interleaves multiple view-based classifiers along the temporal domain in a video sequence. This interleaving technique, combined with a correlation-based tracker, enables fast and robust face tracking over time. Finally, the third component of our system is a keyframe selection method that combines a person classifier with a face classifier. The basic idea is to generate a person keyframe in case the face is not visible, in order to reduce the number of false negatives. We performed quantitatively evaluation of our techniques on standard datasets and on surveillance videos captured by a camera over several days.