A Real-Time Video Tracking System

Gilbert, A. L.; Giles, Michael K.; Flachs, Gerald M.; Rogers, Robert B.; Hsun, U Yee

doi:10.1109/tpami.1980.4766969

Cited by 94 publications

(16 citation statements)

References 4 publications

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“…Several ATD (Automated Target Detection) and tracking algorithms were evaluated [1,2]. Many conventional algorithms (i.e., edge detection) proved ineffective where glare or more than one disk were visible, trackers like AGC (Adaptive Gate Contrast) and Centroid have problems in the presence of ambient light [3].…”

Section: Visionmentioning

confidence: 99%

The University of Texas at Arlington Autonomous Aerial Vehicle?An overview

et al. 1992

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This paper reports on the status of The University of Texas at Arlington student effort to design, build and fly an Autonomous Aerial Vehicle. Both the 1991 entry into the First International Aerial Robotics Competition as well as refinements being made for 1992 are described. Significant technical highlights include a real-time vision system for target objective tracking, a real-time ultrasonic locator system for position sensing, a novel mechanism for gradually moving from human to computer control, and a hierarchical control structure implemented on a 32-bit microcontroller. Detailed discussion about the design of multivariable automatic controls for stability augmentation is included. Position and attitude control loops are optimized according to a combined ~2 and 3~ criteria. We present a modification of a recently published procedure for recovering a desired open-loop transfer function shape within the framework of the mixed ~2/~ problem. This work has led to a new result that frees a design parameter related to imposing the ~-norm constraint. The additional freedom can be used to improve upon the performance and robustness characteristics of the system.

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Section: Visionmentioning

confidence: 99%

The University of Texas at Arlington Autonomous Aerial Vehicle?An overview

et al. 1992

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“…Gilbert used adaptive statistical clustering and projection-based classification algorithms to identify and track objects in the visual field. 32 Corke and Goods proposed a visual servoing controller using a Kalman filter to approximate the motion of a moving target, 33 which was able to track better than using PI control alone. Chaumette and Santos proposed a similar control scheme using a Kalman filter-based estimation of the moving target.…”

Section: Introductionmentioning

confidence: 99%

Panoramic camera tracking on planetary rovers using feedforward control

Ross

Ellery

2017

International Journal of Advanced Robotic Systems

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Future rover missions will be enhanced with the opportunistic search of salient targets during the planetary traverse phase. An essential component of the search is the locating and tracking of targets at the camera control level. The rover visual system must be able to follow quantified information gradients for smooth tracking in the visual field with limited information from images and delayed positional feedback caused by long communication delays inherent in planetary exploration. We propose a control algorithm based on vestibulo-ocular reflexes employed by the human cerebellum. The controller uses a feedback error learning model, which is able to track targets by compensating for the rover motion at the pan-tilt using a network trained prediction of the pan-tilt dynamics. The feedforward controller proved capable in tracking objects in the visual field as was demonstrated in both simulation and on the Barrett WAM.

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“…The degraded video imposes great constraints on a lot of video applications such as video tracking [1], object recognition [2], event detection [3], scene analysis [4], image registration [5], etc. In order to improve the results of these video processing, recently many works have focused on degraded video caused by bad weather.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Probabilistic Framework for Rain Detection

Yao

Wang

Hong

et al. 2014

Entropy

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Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.

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A Real-Time Video Tracking System

Cited by 94 publications

References 4 publications

The University of Texas at Arlington Autonomous Aerial Vehicle?An overview

The University of Texas at Arlington Autonomous Aerial Vehicle?An overview

Panoramic camera tracking on planetary rovers using feedforward control

A Bayesian Probabilistic Framework for Rain Detection

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