Comparison of tracking algorithms implemented in OpenCV

Computer vision is very progressive and modern part of computer science. From scientific point of view, theoretical aspects of computer vision algorithms prevail in many papers and publications. The underlying theory is really important, but on the other hand, the final implementation of an algorithm significantly affects its performance and robustness. For this reason, this paper tries to compare real implementation of tracking algorithms (one part of computer vision problem), which can be found in the very popular library OpenCV. Moreover, the possibilities of optimizations are discussed.Technology Agency of the Czech Republic (TA CR) within the Visual Computing Competence Center - V3C project [TE01020415]; Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency at TBU in Zlin [IGA/FAI/2016/036

Real-time fire detection in camera stream using statistical analysis

The paper describes a new algorithm designed to be fast and efficient for detecting fire. It is based on finding and investigating suspicious regions in each frame of video stream. The investigation consists of tracking regions across frames and performing statistical analysis on their trajectory. If the trajectory has characteristic similar to fire, a test on persistence is performed. If the fire-like characteristics persists, the alarm is triggered. This criterion enables to eliminate a large proportion of false alarms. Given it’s simplicity, the algorithm can be used separately in some environments and can improve existing algorithms as well. © 2017, World Scientific and Engineering Academy and Society. All rights reserved.CZ.1.05/2.1.00/03.0089, ERDF, European Regional Development Fund; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchov

Detecting fire in video stream using statistical analysis

The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware. © The Authors, published by EDP Sciences, 2017

Context sensitive fire protection system

This paper deals with design of context sensitive fire protection system, which should be able to detect and localize fir in industrial areas, especially in large factory halls. The "context sensitive" means that the system will be able to detec the fire, but also recognize the fire type and decide whether the detected fire is intended (as a part of standard productio process) or it is an accident. Moreover, a remote controlled master stream device is part of the designed system; therefore the system can directly start fire-fighting. The main feature of this system will be extremely low level of false positiv actions and minimal collateral damage to the environment surrounding the fire

Detecting fire in video stream using statistical analysis

The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware