Real time tracking of multiple persons by Kalman filtering and face pursuit for multimedia applications (original) (raw)
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Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition, 2005
Robust tracking of multiple people in video sequences is a challenging task. In this paper, we present an algorithm for tracking faces of multiple people even in cases of total occlusion. Faces are detected first; then a model for each person is built. The models are handed over to the tracking module which is based on the mean shift algorithm, where each face is represented by the non-parametric distribution of the colors in the face region. The mean shift tracking algorithm is robust to partial occlusion and rotation, and is computationally efficient, but it does not deal with the problem of total occlusion. Our algorithm overcomes this problem by detecting the occlusion using an occlusion grid, and uses a non-parametric distribution of the color of the occluded person's cloth to distinguish that person after the occlusion ends. Our algorithm uses the speed and the trajectory of each occluded person to predict the locations that should be searched after occlusion ends. It integrates multiple features to handle tracking multiple people in cases of partial and total occlusion. Experiments on a large set of video clips demonstrate the robustness of the algorithm, and its capability to correctly track multiple people even when faces are temporarily occluded by other faces or by other objects in the scene.
Algorithm for multiple faces tracking
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
Robust real-time face tracking is a challenging task. This paper presents an algorithm for tracking faces of multiple people even in case of total occlusion. The method uses the color distribution of each face with the mean shift tracking method. Mean shift tracking is fast and robust to partial occlusion, and it is rotation invariant and computationally efficient. Since the mean shift algorithm does not deal with the problem of total occlusion, we overcome this problem by using an occlusion grid to detect occlusion. We then use the color distribution of the occluded person's clothes to distinguish that person after the occlusion ends. We also use the speed and the trajectory of the occluded person to predict the locations that should be searched after occlusion ends. The proposed face tracking method integrates multiple features to handle tracking of multiple people even in case of occlusion. The experiments show the robustness of the algorithm.
Real-time tracking of multiple persons
2003
Robust tracking of persons in real-world environments and in real-time is a common goal in many video applications. In this paper a computational system for the real-time tracking of multiple persons in natural environments is presented. The system integrates state-of-the-art methodologies for the analysis of movement and color, as well as for the detection of faces. Face detection is complemented by a face tracking module based on heuristics developed by the authors. Exemplary results of the integrated system working in real-world video sequences are shown.
Tracking Multiple People Under Occlusion Using MultipleCameras
Procedings of the British Machine Vision Conference 2000, 2000
We describe a system for tracking multiple people with multiple cameras based on fusion of multiple cues. Face trackers are used to self-calibrate our system. Epipolar geometry and landmarks are employed to disambiguate the tracking problem. The correlation of visual information between different cameras is learnt using Support Vector Regression and Hierarchical Principal Component Analysis to estimate the subject appearance across cameras. The joint features of subjects extracted from multiple cameras are tracked and used as a model to re-track people once the subjects are lost tracking in the system. Results demonstrate that our system can deal with the occlusion.
Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling
1998
A combined 2D, 3D approach is presented that allows for robust tracking of moving bodies in a given environment as observed via a single, uncalibrated video camera. Lowlevel features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that combines lowlevel (image processing) and mid-level (recursive trajectory estimation) information obtained during the tracking process. The resulting system can segment and maintain the tracking of moving objects before, during, and after occlusion. At each frame, the system also extracts a stabilized coordinate frame of the moving objects. This stabilized frame can be used as input to motion recognition modules. The approach enables robust tracking without constraining the system to know the shape of the objects being tracked beforehand; although, some assumptions are made about the characteristics of the shape of the objects, and how they evolve with time. Experiments in tracking moving people are described.
Kalman Filter Based Multiple Person Head Tracking
ArXiv, 2020
For multi-target tracking, target representation plays a crucial rule in performance. State-of-the-art approaches rely on the deep learning-based visual representation that gives an optimal performance at the cost of high computational complexity. In this paper, we come up with a simple yet effective target representation for human tracking. Our inspiration comes from the fact that the human body goes through severe deformation and inter/intra occlusion over the passage of time. So, instead of tracking the whole body part, a relative rigid organ tracking is selected for tracking the human over an extended period of time. Hence, we followed the tracking-by-detection paradigm and generated the target hypothesis of only the spatial locations of heads in every frame. After the localization of head location, a Kalman filter with a constant velocity motion model is instantiated for each target that follows the temporal evolution of the targets in the scene. For associating the targets in ...
Detection and Tracking of Humans and Faces
EURASIP Journal on Image and Video Processing, 2008
We present a video analysis framework that integrates prior knowledge in object tracking to automatically detect humans and faces, and can be used to generate abstract representations of video (key-objects and object trajectories). The analysis framework is based on the fusion of external knowledge, incorporated in a person and in a face classifier, and low-level features, clustered using temporal and spatial segmentation. Low-level features, namely, color and motion, are used as a reliability measure for the classification. The results of the classification are then integrated into a multitarget tracker based on a particle filter that uses color histograms and a zero-order motion model. The tracker uses efficient initialization and termination rules and updates the object model over time. We evaluate the proposed framework on standard datasets in terms of precision and accuracy of the detection and tracking results, and demonstrate the benefits of the integration of prior knowledge in the tracking process.
2014 International Conference on Electronics and Communication Systems (ICECS), 2014
Human tracking is a comprehensive framework for tracking coarse human model performed from sequences of synchronized monocular grayscale images in single or multiple camera system coordinates. It is nothing but segmenting an interested human from video scene and keep track if it continuously. It demonstrates the feasibility of an end to end person tracking system where initially it start background subtraction, then detection of the interested human and tracking of that human form one frame to another continuously. For detection of the interested human PCA algorithm is used. Finally Kalman filter is introduced into tracking the people. Our system have demonstrated that as compaired with other methods it reduces detection time comparitively and improves human detection and tracking accuracy.
Tracking of persons for video surveillance of unattended environments
2008 19th International Conference on Pattern Recognition, 2008
This paper describes a visual surveillance system for remote monitoring of unattended environments. 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.
Tracking of moving human in different overlapping cameras using Kalman filter optimized
EURASIP Journal on Advances in Signal Processing
Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by usin...