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

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

J. Leu

Author Tools

Chat about Author

Papers

Using shape feature matching to track moving objects in image sequences