Person tracking with partial occlusion handling (original) (raw)
Related papers
OCCLUSION-HANDLING FOR IMPROVED PARTICLE FILTERING-BASED TRACKING
2009
One of the particle filtering uses is object tracking since this technique permits to deal with uncertainty over time met in real time image sequences framework. This uncertainty is as much nonmanageable that an object occlusion appears in images. In this paper, we propose an occlusion-handling scheme which significantly improves the tracking performance in presence of partial occlusion. The proposed technique is applied to track a single object in real greyscale image sequences. Results confirm tracking performance enhancement.
2015
Occlusions, one of the most challenging problems in visual tracking, degrade the performance of many trackers significantly. Taking various spatial and temporal forms, occlusions have not been modeled completely yet. State-of-the-art solutions fail to handle persistent and complex occlusions, and mostly address partial or temporal occlusions. Additionally, the solutions around these problems are not unified, and researchers limit their solutions to a tiny portion of the problem. Despite the large number of studies of handling occlusion, only a few of them have actually studied the occlusion phenomenon itself and devised solutions for occlusion detection and reasoning. Any comprehensive study over different approaches of occlusion handling is deemed missing. To address this shortcoming, this study first presents a comprehensive review on the literature. The occlusion problem is defined, its challenges are described, and several research directions to handle it are distinguished. Next...
2014
This document contains the supplementary material for the paper submitted to “Pattern Recognition Letters”. Qualitative and quantitative analysis of the tracker performance for different scenarios is presented in this document. Additionally the details of the robust feature calculation section is presented in this document. c © 2014 Elsevier Ltd. All rights reserved. 1. Detailed Analysis of Videos In this section, the results of all trackers over all five videos used in the paper are presented. Following a brief overview of the dataset configuration and rival algorithms, the videos are presented one-by-one with a quick review on their characteristics. Then the result of the trackers are presented along with a discussion over the performance of them. As mentioned before, this study uses five videos from Princeton Tracking Dataset (Song and Xiao, 2013). Authors of this paper aimed to standardize a uniform evaluation criteria for tracker comparison, having occlusion evaluation in mind....
An Adaptive Particle Filtering for Solving Occlusion Problems of Video Tracking
Communications in Computer and Information Science, 2015
In recent years, the visual object tracking has drawn increasing interests. There are many applications, e.g., video surveillance in airports, schools, hospitals and traffic. The object surveillance may provide crucial information about the behavior, interaction, and relationship between objects of interest. This paper addresses issues in object tracking where videos contain complex scenarios. We propose an adaptive particle filters tracking scheme with exquisite resampling (AERPF), which improves prediction, importance sampling and resampling. In prediction step, an adaptive strategy for search region and particle number is addressed for object disappearing or obstacle disturbance, which can obtain results more effectively. In addition, in importance sampling, we use optical flow to refine the particle weights using the dynamical object motion information, which results the better accuracy of object location updating. Moreover, exquisite resampling (ER) algorithm can be applied for reflecting more the posterior probability density function of true state. The proposed method can be applied for object tracking both on fixed and active camera, handling partial occlusion and full occlusion problem properly. As a result, it outperforms other existing methods.
Improving Tracking by Handling Occlusions
Lecture Notes in Computer Science, 2005
Keeping track of a target by successive detections may not be feasible, whereas it can be accomplished by using tracking techniques. Tracking can be addressed by means of particle filtering. We have developed a new algorithm which aims to deal with some particle-filter related problems while coping with expected difficulties. In this paper, we present a novel approach to handling complete occlusions. We focus also on the target-model update conditions, ensuring proper tracking. The proposal has been successfully tested in sequences involving multiple targets, whose dynamics are highly non-linear, moving over clutter.
Occlusion-Robust Pedestrian Tracking in Crowded Scenes
2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015
This paper focuses on tracking in typical traffic monitoring scenarios with emphasis on handling occlusions caused by trees, lampposts and cables. We extend the existing TRacking with Occlusion handling and Drift correction (TROD) algorithm with a novel occlusion detection algorithm, based on measuring the changes in the object motion pattern. The motion information is extracted via frame differencing and described a the HOG descriptor. Occlusions are handled by preventing the model update and predicting the object location based on prior observations. Our proposed system clearly outperforms state-of-the-art tracking algorithms for larger occlusions in the specific pedestrian surveillance scenario, that is, the percentage of successfully tracked objects grows with 10-15%. At the same time, for non-specific public datasets, the performance is similar to existing state-of-the-art tracking algorithms.
Human Tracking using Particle Filter
International Journal of Computer Applications, 2013
Human tracking is the process of locating moving objects (human) over time using camera. It has wide number of applications like security and surveillance, traffic control, video editing, medical imaging etc. It can be a time consuming process due to the large amount of data contained in video. The objective of human tracking is to associate target objects in consecutive video frames. To initiate human tracking an algorithm analyzes video frames and outputs the movement of targets between the frames. There are a number of algorithms each having its own strengths and weakness. Considering the intended use is important when choosing the algorithm. This paper proposes particle filter based methods for human tracking, addressing two major issues such as variations of distance measurement (similarity measure) and Re-Sampling algorithms.
Hybrid blob and particle filter tracking approach for robust object tracking
Procedia Computer Science, 2010
Analysing and characterising human behaviour is now receiving much attention from the visual surveillance research community. Generally, human behaviour recognition requires human to be detected and tracked so that the trajectory patterns of the human can be captured and analysed for further interpretation. Therefore, it is crucial for tracking algorithms to be fast and robust to partial and short-life occlusion. In addition, the detection of object-of-interest to be tracked should be automated, without the need for manual intervention. This paper thus proposes a tracking system targeted for real time surveillance applications that integrate blob and simplified particle filter tracking approaches so as to exploit the advantages of both approaches while minimizing their respective disadvantages. The blob approach acts as the main tracking and will invoke the simplified particle filter tracking in the event of blob merging or occlusion. In this paper, the proposed tracking method is tested using PETS 2009 sequences to illustrate the capability of solving occlusion and obstruction in the scene. The results show that the proposed system successfully tracks objects during and after occlusion with other objects or after obstructed by the background.
Tracking of Occluded and Cluttered Objects
Object tracking in video is the identification of one or more target objects, once recognized in a video frame, in subsequent frames. A classifier based multi object tracking framework is presented with occlusion handling. The basic idea is to detect a class of objects in a video and pass the detected objects, in the form of bounding boxes, to the tracker. The tracker then discriminates the objects and keeps their individual tracks. Also, should the detector miss an object that was detected in the previous frame, the tracker attempts to locate it. A people tracker is implemented using the framework HOG (Histogram of Oriented Gradients) based classifier is used for people detection. Color histogram, edge and ORB (Oriented FAST and Rotated BRIEF) features are used, individually and in combination (color + ORB), in the implementation.
Part-based multiple-person tracking with partial occlusion handling
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM classifiers which capture the articulations of the human bodies in dynamically changing appearance and background. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection, which significantly improves the detection performance in crowded scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions, and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking.