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Real-time people tracking for mobile robots using thermal vision - rakeshshinagne - 08-16-2017 Real-time people tracking for mobile robots using thermal vision [attachment=760] Abstract This paper presents a vision-based approach for tracking people on a mobile robot using thermal images. The approach combines a particle filter with two alternative measurement models that are suitable for real-time tracking. With this approach a person can be detected independently from current light conditions and in situations where no skin colour is visible. Introduction As research robots move closer towards real applications in populated environments, these systems will require robust algorithms for tracking people to ensure safe human robot cohabitation. The tracking problem is well-known in computer vision, where there are developed systems used mostly in surveillance and identity verification. Traditionally, tracking multiple people has been investigated for surveillance applications with one or more static cameras (see e.g. [16]) and in automatic motion capturing and human motion analysis [24, 27]. In these environments, moving objects can be easily detected using background subtraction techniques. Related work Feyrer and Zell [11] combine skin colour, shape, motion and depth information to track a single person that faces the robot. This approach is combined with information from a laser scanner by the same authors in [12]. Schlegel et al. [30] extract a colour histogram from the shirt of a person and combine colour blob tracking with a contour-based approach. Tracking of the head shoulder contour and the hand silhouette is used in [23] to build a human robot interface. Colour histograms, optical flow and an extension of the background subtraction technique is used by Zajdel et al. [38] who developed a system that keeps track of humans who leave the field of view of the robot and re-enter. Evaluation This section presents the evaluation methodology used in our experiments. First we describe the format and process of acquiring ground truth data, the training procedure, then the metrics used for evaluation and finally experimental results. Conclusions and future work This paper presented a people tracking system for mobile robots using thermal vision and provided a thorough evaluation of its performance. The system uses a robust and fast tracking method based on a particle filter and several different measurement models that are very fast to calculate (contourbased, integral image features and a combination of the two). We determined the optimal values of different system parameters. |