Keywords
Collaborative Robots
Work Area, Computer Vision,
CAMShift Algorithm, Tracking People
How to Cite
Abstract
This article considers the complex implementation of the CAMShift algorithm for human tracking in the collaborative robot working area. The study covers both the algorithmic and mathematical underpinnings of CAMShift, detailing the underlying principles and mathematical models used to improve tracking accuracy. A Python program was developed in the PyCharm environment to effectively implement this algorithm, taking into account aspects such as real-time processing and integration with robotic systems. The research conducted a comprehensive assessment of the tracking speed, studied how effectively the algorithm works in different conditions and how it affects the overall sensitivity of the system. The results demonstrate the effectiveness of the CAMShift algorithm in providing accurate and timely tracking, highlighting its suitability for dynamic and interactive environments. This work helps to optimize the performance of collaborative robots by improving tracking capabilities, enabling better interaction and safety in shared work areas.
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