Extraction of 3D Features from Complex Environments in Visual Tracking Applications

In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.

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HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter

Sensors ◽

10.3390/s20072137 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2137 ◽

Cited By ~ 2

Author(s):

Chenpu Li ◽

Qianjian Xing ◽

Zhenguo Ma

Keyword(s):

Kalman Filter ◽

Visual Tracking ◽

Prior Information ◽

State Of The Art ◽

The Other ◽

Robust Tracking ◽

Similarity Learning ◽

Complex Environments ◽

Color Information ◽

Learning Problem

In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC’s disadvantages. One model contained the target’s prior color information, and the other the target’s prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram–Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker’s performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

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Fuzzy Detection aided Real-time and Robust Visual Tracking under Complex Environments

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2020.3006520 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 12

Author(s):

Shuai Liu ◽

Shuai Wang ◽

Xinyu Liu ◽

Chin-Teng Lin ◽

Zhihan Lv

Keyword(s):

Real Time ◽

Visual Tracking ◽

Complex Environments

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Target Contour Recovering for Tracking People in Complex Environments

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/506908 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10

Author(s):

Jianhua Zhang ◽

Sheng Liu ◽

Y. F. Li ◽

Jianwei Zhang

Keyword(s):

Visual Tracking ◽

Video Sequence ◽

Tracking System ◽

Target Object ◽

Detection Methods ◽

Partial Occlusion ◽

Occlusion Detection ◽

Challenging Problem ◽

Complex Environments ◽

Novel Method

Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours.

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A Robust Visual Tracking Method for Unmanned Mobile Systems

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4043119 ◽

2019 ◽

Vol 141 (7) ◽

Author(s):

Xiongfeng Yi ◽

Zheng Chen

Keyword(s):

Visual Tracking ◽

Water Surface ◽

Mobile Systems ◽

Light Reflection ◽

Image Process ◽

Water Tank ◽

Complex Environments ◽

Tracking Method ◽

Real Time Tracking ◽

Image Pixels

This paper introduces a robust visual tracking of objects in complex environments with blocking obstacles and light reflection noises. This visual tracking method utilizes a transfer matrix to project image pixels back to real-world coordinates. During the image process, a color and shape test is used to recognize the object and a vector is used to represent the object, which contains the information of orientation and body length of the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector predicts the position of the object. During the real-time tracking, a Kalman filter is used to optimize the result. To validate the method, the visual tracking algorithm was tested by tracking a submarine and a fish on the water surface of a water tank, above which three pieces of blur glass were blocking obstacles between the camera and the object. By using this method, the interference from the reflection of the side glass and the fluctuation of the water surface can be also avoided.

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