The paper describes a robotic application that tracks a moving object by utilizing a mobile robot with capacity of avoiding obstacles. Real-time tracking algorithm is based on mobile robot vision using adaptive color matching and Kalman filter. Adaptive color matching is used to obtain motion vectors of moving object in the robot coordinate system. It can adjust color matching threshold adaptively to reduce the influence of lighting variations in the scene. Kalman filter is applied to design a view window. View window, which contains the next position of the moving object estimated by Kalman filter, is determined on image plane to reduce the image processing area. Color matching threshold can adjust itself adaptively in view window. A virtual targets based algorithm is also presented for real-time obstacle avoidance in this paper. Experimental results show that the tracking algorithm can adapt to lighting variations and has good tracking precision. It can also avoid obstacles smoothly while tracking moving object. Furthermore, it can be implemented in real time.

Moving object tracking based on mobile robot vision

Change detection using a statistical model in an optimally selected color space

We present a novel change detection method using a statistical model of the image noise. Most change detection methods are based on gray-level images. However, color images can provide much richer scene information. One major problem to use the color images in change detection is how to combine three components in color space as a detection cue. We use the Euclidean color distance of three channels to measure the difference between two consecutive images. Specifically, we present a new noise model for each color channel. Through this modeling we can estimate the distribution of the Euclidean color distance for unchanged regions. We can find the optimal threshold to detect changes using this estimated distribution. Although we use the optimal threshold, inevitably there may be false classifications. To reject these erroneous cases, we adopt the graph cuts method that efficiently minimizes the global energy, which takes into account the effect of neighboring pixels.

Change detection using a statistical model of the noise in color images

The paper proposes a real-time tracking algorithm for a moving object with mobile robot based on vision using adaptive color matching and Kalman filter. The adaptive color matching can limit the region containing moving object on vision image plane. It can adjust color matching threshold to reduce the influence of lighting variations in the scene. Kalman filter is used as our prediction module to calculate motion vectors of moving object in the robot coordinate system. A view window containing the position of moving object estimated by Kalman filter is determined on image plane to reduce the image processing area. Color matching threshold can adjust itself adaptively in view window, which is used as an updating module. Experimental results show that the algorithm can adapt to lighting variations and has good tracking precision. It can also be implemented in real time.

Tracking a moving object with mobile robot based on vision

The maintenance of relevant backgrounds under various scene changes is very crucial to detect foregrounds robustly. We propose a background maintenance method for dynamic scenes including global intensity level changes caused by changes of illumination conditions and camera settings. If the global level of the intensity changes abruptly, the conventional background models cannot discriminate true foreground pixels from the background. The proposed method adaptively modifies the background model by estimating the level changes. Because there are changes caused by moving objects as well as global intensity level changes, we estimate the dominant level change over the whole image regions by mean shift. Then, the problem caused by saturated pixels are handled by an additional scheme. In the experiments for dynamic scenes, our proposed method outperforms previous methods by adaptive background maintenance and handling of saturated pixels.

Robust background maintenance by estimating global intensity level changes for dynamic scenes

The paper develops a methodology to track moving objects in image sequences. The methodologies in tracking moving objects are influenced by the natural environment and motion characteristics. This paper introduces an algorithm to track moving objects under a natural environment (high noise ratio background). This algorithm can be applied from low noise ratio background to high noise ratio background. It also provides adjustable parameters to fit into different situations. It makes this algorithm flexible. The algorithm extracts moving objects by the difference between consecutive image frames and analyzes the shape features of moving objects. After shape analysis, the different moving objects are distinguished based on different shape feature characteristics including motion coordinate, speed, and direction. The original moving path can be found by coordinate transform. Some restrictions are applied. The moving object has to be a rigid body. The moving path has to be uni-directional. One camera is used which has to be stationary to take image sequences.

Using shape feature matching to track moving objects in image sequences

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Using shape feature matching to track moving objects in image sequences