Authors:
M. Nilsson
;
H. Ardö
;
A. Laureshyn
and
A. Persson
Affiliation:
Lund University, Sweden
Keyword(s):
Bicycle Detection, Search Space, RANSAC, SMQT, Split up SNoW.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Video Analysis
Abstract:
This paper describes a solution to the application of rapid detection of bicycles in low resolution video. In particular, the application addressed is from video recorded in a live environment. The future aim from the results in this paper is to investigate a full year of video data. Hence, processing speed is of great concern. The proposed solution involves the use of an object detector and a search space reduction method based on prior knowledge regarding the application at hand. The method using prior knowledge utilizes random sample consensus, and additional statistical analysis on detection outputs, in order to define a reduced search space. It is experimentally shown that, in the application addressed, it is possible to reduce the full search space by 62% with the proposed methodology. This approach, which employs a full detector in combination with the design of a simple and fast model that can capture prior knowledge for a specific application, leads to a reduced search space
and thereby a significantly improved processing speed.
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