Adaptive multi-cue based particle swarm optimization guided particle filter tracking in infrared videos

Track-before-detect algorithm based on the particle filter algorithm has the problems of low tracking precision, poor particles, and requiring a large amount of particles to be calculated in a low signal-to-noise ratio, which is difficult to meet the accuracy and speed required by the modern infrared search and tracking system. In this paper, an improved infrared small target detection and tracking method based on a new particle filter is proposed. This is where particles are used to represent an individual bat to imitate the hunting process of bats. By adjusting loudness, frequency, and impulse emissivity of a particle swarm, the optimal particle at that time is followed to search in the solution space. In addition, the global search and the local search can also be dynamically switched to improve the quality and distribution of the particle swarm. The performance of the proposed algorithm is tested in a simulation scene and the real scene of the infrared small target detection and tracking. Experimental results show that the proposed algorithm improves the performance of the infrared searching and tracking system.

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“…Choosing the univariate nonstationary growth model, and the process model and measurement model are given as Formulas (34) and (35), ie,…”

Section: Test Of Basic Performance Improved Filtermentioning

confidence: 99%

Improved infrared small target detection and tracking method based on new intelligence particle filter

Chen¹,

Tian

et al. 2017

Computational Intelligence

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“…The genetic algorithm operations such as selection, crossover, and mutation operation were used for solving sample impoverishment. In [32], Miahui Zhang et al used particle swarm optimization for solution of sample impoverishment and of adaptive fusion of weights from intensity and gradient cue. However, results were presented for infrared video under limited environments conditions.…”

Section: Related Workmentioning

confidence: 99%

Robust object tracking based upon adaptive multi-cue integration for video surveillance

Walia¹,

Kapoor

2015

Multimed Tools Appl

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It was well argued in literature that integrating multi-cue increases accuracy and robustness of visual tracking. Although, multi-cues object tracking using singlemodal or multimodal was explored by some of researchers, it still remains an open challenge to fuse multi-cue from different modularity under dynamic environment conditions. The aim of this research paper is to introduce a novel multi-cue object tracking framework using particle filter. In particle filter framework, our approach integrates cues while evaluating each particle instead of primitive approach of deciding cues performance in current frame based upon either a few present particles or previous state particles. First, we model our multi-cue tracking framework using Shafer's model and multi-cue data is combined using Conjunctive combination rules. The partial and total conflict among cues at particle level is redistributed efficiently using Proportional Conflict Redistribution (PCR-5) rules. In proposed model, automatic suppression/ boosting of particles along with online conflict resolving facilitate resampling process for efficiently handling of particle degeneracy. Most importantly, compared to other state-of-art trackers, our proposed algorithm can handle more efficiently various dynamic environments conditions such as partial or full occlusion, illumination changes, weather, and visibility. In this manuscript, we demonstrate our proposed adaptive multi-cue fusion model on challenging benchmark video and thermal sequences and compare tracking results of our tracker with stateof-art trackers.

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“…Estimation-based or generative algorithms model the target based on appearance features and then search for it in each frame of the visual stream [ 11 ]. Examples of this type of algorithm, which are mainly driven by innovations in appearance models, include MeanShift [ 12 ], particle filter-based algorithms [ 13 ], Incremental Visual Tracking (IVT) [ 14 ] and optical flow-based algorithms [ 15 ]. Classification-based or discriminative algorithms treat tracking as a binary pattern recognition problem and try to separate the target from the background [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Kinect-Based Real-Time Compressive Tracking Prototype System for Amphibious Spherical Robots

Pan

Shi

Guo

2015

Sensors

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A visual tracking system is essential as a basis for visual servoing, autonomous navigation, path planning, robot-human interaction and other robotic functions. To execute various tasks in diverse and ever-changing environments, a mobile robot requires high levels of robustness, precision, environmental adaptability and real-time performance of the visual tracking system. In keeping with the application characteristics of our amphibious spherical robot, which was proposed for flexible and economical underwater exploration in 2012, an improved RGB-D visual tracking algorithm is proposed and implemented. Given the limited power source and computational capabilities of mobile robots, compressive tracking (CT), which is the effective and efficient algorithm that was proposed in 2012, was selected as the basis of the proposed algorithm to process colour images. A Kalman filter with a second-order motion model was implemented to predict the state of the target and select candidate patches or samples for the CT tracker. In addition, a variance ratio features shift (VR-V) tracker with a Kalman estimation mechanism was used to process depth images. Using a feedback strategy, the depth tracking results were used to assist the CT tracker in updating classifier parameters at an adaptive rate. In this way, most of the deficiencies of CT, including drift and poor robustness to occlusion and high-speed target motion, were partly solved. To evaluate the proposed algorithm, a Microsoft Kinect sensor, which combines colour and infrared depth cameras, was adopted for use in a prototype of the robotic tracking system. The experimental results with various image sequences demonstrated the effectiveness, robustness and real-time performance of the tracking system.

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Adaptive multi-cue based particle swarm optimization guided particle filter tracking in infrared videos

Cited by 27 publications

References 19 publications

Improved infrared small target detection and tracking method based on new intelligence particle filter

Improved infrared small target detection and tracking method based on new intelligence particle filter

Robust object tracking based upon adaptive multi-cue integration for video surveillance

A Kinect-Based Real-Time Compressive Tracking Prototype System for Amphibious Spherical Robots

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