Robust Occlusion Handling in Object Tracking

Pan, Jiyan; Hu, Bo

doi:10.1109/cvpr.2007.383453

Cited by 74 publications

(50 citation statements)

References 12 publications

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“…Pan and Hu propose content adaptive progressive occlusion analysis algorithm for object tracking with occlusion handling. The entire Region of interest is determined, then the occlusion status of the block containing undetermined pixels are analyzed and finally all such blocks are scanned [5]. Ramik, Sabourin, Moreno and Madani developed an intelligent vision system that has the ability to learn autonomously individual objects present in a real environment without using human labeled data.…”

Section: Diverse Real World Occluded Scenementioning

confidence: 99%

Occlusion Detection and Handling: A Review

Chandel¹,

Vatta²

2015

IJCA

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Object tracking and detection is a classical research area in the field of computer vision from decades. Numerous kinds of applications are dependent on the area of object detection, such as advance driving assistance system, traffic surveillance, scene understanding, autonomous navigation etc. Many challenges still exist while detecting an object such as illusion, low visibility, cast shadows and most importantly occlusions of object. Occlusions occur under two categories, firstly its, self-occlusion which means that, from a certain viewpoint, one part of an object is occluded by another part. Secondly, its inter-object occlusion which means when two objects being tracked occlude each other. We will review various occlusion handling methods that involved single and multiple cameras according to their application. In short, the objective of this paper is to deliberate in detail the problem of occlusion in object tracking and provide a concise review for the problem of occlusion handling under different categories and identify new trends.

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Section: Diverse Real World Occluded Scenementioning

confidence: 99%

Occlusion Detection and Handling: A Review

Chandel¹,

Vatta²

2015

IJCA

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“…In Ref. [11], it presents a novel method for cost aggregation and occlusion handling for stereo matching. In order to estimate optimal cost, given a per-pixel difference image as observed data, we define an energy function and solve the minimization problem by solving the iterative equation with the numerical method.…”

Section: Spatio-temporal Approaches For Handling Occlusions Based On mentioning

confidence: 99%

Spatio-Temporal Approaches for Handling Occlusions Based on Object Tracking

Se¹,

Diala²

2017

J Comput Sci Syst Biol

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“…Evidence is gathered from all of the cameras into a synergistic framework. Other approaches are based on examining the measurement error for each pixel [22,23]. The pixels that their measurement error exceeds a certain value are considered to be occluded.…”

Section: Introductionmentioning

confidence: 99%

“…This problem is circumvented in [22], where the image region that matches the best with the template over a prefixed duration is assumed to be the reappearing target. In [23], an observation model and a velocity motion model were defined. The observation model was based on an adaptive appearance model, and the velocity motion model was derived using a first-order linear predictor.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic integrated object recognition and tracking framework

Serratosa

Alquézar

Amézquita

2012

Expert Systems with Applications

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Abstract. 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 colour 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.

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Robust Occlusion Handling in Object Tracking

Cited by 74 publications

References 12 publications

Occlusion Detection and Handling: A Review

Occlusion Detection and Handling: A Review

Spatio-Temporal Approaches for Handling Occlusions Based on Object Tracking

A probabilistic integrated object recognition and tracking framework

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