839 research outputs found

    A Particle Swarm Optimization inspired tracker applied to visual tracking

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    International audienceVisual tracking is dynamic optimization where time and object state simultaneously influence the problem. In this paper, we intend to show that we built a tracker from an evolutionary optimization approach, the PSO (Particle Swarm optimization) algorithm. We demonstrated that an extension of the original algorithm where system dynamics is explicitly taken into consideration, it can perform an efficient tracking. This tracker is also shown to outperform SIR (Sampling Importance Resampling) algorithm with random walk and constant velocity model, as well as a previously PSO inspired tracker, SPSO (Sequential Particle Swarm Optimization). Experiments were performed both on simulated data and real visual RGB-D information. Our PSO inspired tracker can be a very effective and robust alternative for visual tracking

    Object Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially occluded Targets

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    Motion estimation, object detection, and tracking have been actively pursued by researchers in the field of real time video processing. In the present work, a new algorithm is proposed to automatically detect objects using revised local binary pattern (m-LBP) for object detection. The detected object was tracked and its location estimated using the Kalman filter, whose state covariance matrix was tuned using particle swarm optimisation (PSO). PSO, being a nature inspired algorithm, is a well proven optimization technique. This algorithm was applied to important real-world problems of partially-occluded objects in infrared videos. Algorithm validation was performed by realizing a thermal imager, and this novel algorithm was implemented in it to demonstrate that the proposed algorithm is more efficient and produces better results in motion estimation for partially-occluded objects. It is also shown that track convergence is 56% faster in the PSO-Kalman algorithm than tracking with Kalman-only filter

    Perceiving user's intention-for-interaction: A probabilistic multimodal data fusion scheme

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    International audienceUnderstanding people's intention, be it action or thought, plays a fundamental role in establishing coherent communication amongst people, especially in non-proactive robotics, where the robot has to understand explicitly when to start an interaction in a natural way. In this work, a novel approach is presented to detect people's intention-for-interaction. The proposed detector fuses multimodal cues, including estimated head pose, shoulder orientation and vocal activity detection, using a probabilistic discrete state Hidden Markov Model. The multimodal detector achieves up to 80% correct detection rates improving purely audio and RGB-D based variants

    A robust tracking system for low frame rate video

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    Tracking in low frame rate (LFR) videos is one of the most important problems in the tracking literature. Most existing approaches treat LFR video tracking as an abrupt motion tracking problem. However, in LFR video tracking applications, LFR not only causes abrupt motions, but also large appearance changes of objects because the objects’ poses and the illumination may undergo large changes from one frame to the next. This adds extra difficulties to LFR video tracking. In this paper, we propose a robust and general tracking system for LFR videos. The tracking system consists of four major parts: dominant color-spatial based object representation, bin-ratio based similarity measure, annealed particle swarm optimization (PSO) based searching, and an integral image based parameter calculation. The first two parts are combined to provide a good solution to the appearance changes, and the abrupt motion is effectively captured by the annealed PSO based searching. Moreover, an integral image of model parameters is constructed, which provides a look-up table for parameters calculation. This greatly reduces the computational load. Experimental results demonstrate that the proposed tracking system can effectively tackle the difficulties caused by LFR
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