Dealing with occlusion in a probabilistic object tracking method (original) (raw)
Related papers
A probabilistic integrated object recognition and tracking framework
Expert Systems with Applications, 2012
This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.
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.
The State-of-the-Art in Handling Occlusions for Visual Object Tracking
IEICE Transactions on Information and Systems, 2015
This paper reports on the trending literature of occlusion handling in the task of online visual tracking. The discussion first explores visual tracking realm and pinpoints the necessity of dedicated attention to the occlusion problem. The findings suggest that although occlusion detection facilitated tracking impressively, it has been largely ignored. The literature further showed that the mainstream of the research is gathered around human tracking and crowd analysis. This is followed by a novel taxonomy of types of occlusion and challenges arising from it, during and after the emergence of an occlusion. The discussion then focuses on an investigation of the approaches to handle the occlusion in the frame-by-frame basis. Literature analysis reveals that researchers examined every aspect of a tracker design that is hypothesized as beneficial in the robust tracking under occlusion. State-of-the-art solutions identified in the literature involved various camera settings, simplifying assumptions, appearance and motion models, target state representations and observation models. The identified clusters are then analyzed and discussed, and their merits and demerits are explained. Finally, areas of potential for future research are presented.
Occlusion Handling in Object Detection
2012
Object tracking is a process that follows an object through consecutive frames of images to determine the object's movement relative other objects of those frames. In other words, tracking is the problem of estimating the trajectory of an object in the image plane as it moves around a scene. This chapter presents research that deals with the problem of tracking objects when they are occluded. An object can be partially or fully occluded. Depending on the tracking domain, a tracker can deal with partial and full object occlusions using features such as colour and texture. But sometimes it fails to detect the objects after occlusion. The shape feature of an individual object can provide additional information while combined with colour and texture features. It has been observed that with the same colour and texture if two object's shape information is taken then these two objects can be detected after the occlusion has occurred. From this observation, a new and a very simple algorithm is presented in this chapter, which is able to track objects after occlusion even if the colour and textures are the same. Some experimental results are shown along with several case studies to compare the effectiveness of the shape features against colour and texture features.
Feature Based Object Tracking: A Probabilistic Approach
2016
Video analysis is a rich research topic, due to the wide spectrum of applications such as surveillance, activity recognition, security, and event detection. One of the important challenges in video analysis is object tracking, which provides the ability to determine the exact location of an object of interest within each frame. Many challenges affect the efficiency of a tracking algorithm such as scene illumination change, occlusion, scaling change and determining a search window from which to track object(s). We present an integrated probabilistic model for object tracking, that combines implicit dynamic shape representations and probabilistic object modeling. We demonstrate the proposed tracking algorithm on a benchmark video tracking data set, and achieve state-of-the art results in both overlap-accuracy and speed.
A Comparative Study of different Oject Tracking Methods in a Video
International Journal of Computer Applications
Visual Object Tracking (VOT) is the most salient and an ongoing exploration field amongst the several disciplines of computer vision. The importance of this technology is due to the extensive range of applications such as robot navigation, human computer interaction, video surveillance, etc. The process of object tracking involves segmenting areas of a video scene and tracking its position, motion and occlusion. However, problems can appear during tracking on account of multiple issues including camera motion, object-to-object and object-to-scene occlusions, nonrigid structures, object and scene changes in patterns and appearance and abrupt object movement. The aim of this paper is to examine, analyze and provide a shortlist of the most ubiquitous object tracking techniques. This accomplish by providing a comprehensive review of the tracking process which involve object detection methods, object representation and features selection and object tracking over multiple frames. Object tracking methods are compared whilst elaborating upon the advantages and limitations.
2004
Abstract In this paper, an algorithm for tracking multiple rigid and non-rigid objects in conditions of occlusion is presented. The proposed method is based on a scalable and adaptive model based on joint information of color and shape. Through a GHT (generalized Hough transform) based voting method the center of mass of each object can be determined in real time with a good degree of precision. Quantitative and qualitative results are presented to validate the efficiency of the method
Tracking Occluded Objects Using Kalman Filter and Color Information
2014
Robust visual tracking is imperative to track multiple occluded objects. Kalman filter and color information tracking algorithms are implemented independently in most of the current research. The proposed method combines extended Kalman filter with past and color information for tracking multiple objects under high occlusion. The proposed method is robust to background modeling technique. Object detection is done using spatio-temporal Gaussian mixture model (STGMM). Tracking consists of two steps: partially occluded object tracking and highly occluded object tracking. Tracking partially occluded objects, extended Kalman filter is exploited with past information of object, whereas for highly occluded object tracking, color information and size attributes are used. The system was tested in real world application and successful results were obtained.
Tracking video objects in cluttered background
2005
Abstract We present an algorithm for tracking video objects which is based on a hybrid strategy. This strategy uses both object and region information to solve the correspondence problem. Low-level descriptors are exploited to track object's regions and to cope with track management issues. Appearance and disappearance of objects, splitting and partial occlusions are resolved through interactions between regions and objects.