Background Modelling in Infrared and Visible Spectrum Video for People Tracking

Conaire, Ciarán Ó; Cooke, Eddie; O’Connor, Noel E.; Murphy, Noel; Smearson, A.

doi:10.1109/cvpr.2005.419

Cited by 19 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve these problems, many authors researched various methods to improve background subtraction method based on GMMs. To improve the adaptability of the system to illumination changes [31], modified the update equation [32], explored 3D multi-variable Gaussian distribution, and [33] suggested a method that can automatically calculate the number of proper Gaussian distributions of each pixel instead of setting a constant. A recent method [34] proposed a new framework integrating hysteresis thresholding and motion compensation.…”

Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

View full text Add to dashboard Cite

Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

View full text Add to dashboard Cite

“…This technique has been used by Fujimasa 11 and others in medical imagery, and several others in human detection and tracking 11,12 . We employ similar fusion in verification of human presence.…”

Section: Color-thermal Fusionmentioning

confidence: 99%

“…This is done by segmenting regions of temperature consistent with human presence from imagery. We followed the approach developed by Conaire which employs image histograms to select regions with temperatures likely to be produced by humans from the background 11 . This method performs reliably in most environments.…”

Section: Verificationmentioning

confidence: 99%

Sensor fusion for intelligent behavior on small unmanned ground vehicles

et al. 2007

View full text Add to dashboard Cite

Sensors commonly mounted on small unmanned ground vehicles (UGVs) include visible light and thermal cameras, scanning LIDAR, and ranging sonar. Sensor data from these sensors is vital to emerging autonomous robotic behaviors. However, sensor data from any given sensor can become noisy or erroneous under a range of conditions, reducing the reliability of autonomous operations. We seek to increase this reliability through data fusion. Data fusion includes characterizing the strengths and weaknesses of each sensor modality and combining their data in a way such that the result of the data fusion provides more accurate data than any single sensor. We describe data fusion efforts applied to two autonomous behaviors: leader-follower and human presence detection. The behaviors are implemented and tested in a variety of realistic conditions. KEYWORDS: robotics, unmanned systems, data fusion, intelligent behaviors, computer vision 1. BACKGROUND Technology Transfer ProjectThe JGRE Technology Transfer Project (TechTXFR) managed by Space and Naval Warfare Systems Center, San Diego (SSC San Diego) seeks to enhance the functionality (ability to perform more tasks) and autonomy (with less human intervention) of teleoperated systems 1 . The objective is to expedite advancement of the technologies needed to produce an autonomous robot that can robustly perform in battlefield situations. Instead of developing new capabilities from scratch, the approach is to assess the technology readiness levels (TRLs) of component technologies (i.e., mapping, object recognition, motion-detection-on-the-move) developed under a variety of past and ongoing R&D efforts (such as the DARPA Tactical Mobile Robot program). The most mature algorithms are integrated and optimized into cohesive behavior architectures and then ported to various platforms used by the warfighter for further evaluation in operational environments. Contributing sources of component technologies include the Idaho National Laboratory (INL), NASA's Jet PropulsionLaboratory, Carnegie-Mellon University (CMU), Stanford Research Institute International (SRI), University of Michigan, Brigham Young University, University of California San Diego, and University of Texas Austin, as well as other SSC San Diego projects (e.g., Man Portable Robotic System 2 and the ROBART series 3 ). Starting in FY-03, the approach was to harvest existing indoor navigation technologies developed by various players and assess their different approaches to dead reckoning, obstacle detection/avoidance, mapping, localization, and path planning. The details of these focus areas will not be discussed in this paper but can be found in previous project publications 4 . The best features of the more promising solutions have now been integrated into an optimal system, giving an operator the ability to send an autonomous platform into an unknown indoor area and accurately map the surroundings. An augmented virtuality representation of the environment is derived, fusing real-time sensor information with the evo...

show abstract

“…Davis and Sharma [2], as well as O'Conaire et al [5], used an infinite planar homography assumption to perform registration. Under this assumption, the imaged scene will be very far from the camera, so that an object's displacement from the registered ground plane will be negligible compared to the observation distance.…”

Section: Related Researchmentioning

confidence: 99%

Registering Multimodal Imagery with Occluding Objects Using Mutual Information: Application to Stereo Tracking of Humans

Krotosky

Trivedi

Augmented Vision Perception in Infrared

View full text Add to dashboard Cite

This chapter introduces and analyzes a method for registering multimodal images with occluding objects in the scene. An analysis of multimodal image registration gives insight into the limitations of assumptions made in current approaches and motivates the methodology of the developed algorithm. Using calibrated stereo imagery, we use maximization of mutual information in sliding correspondence windows that inform a disparity voting algorithm to demonstrate successful registration of objects in color and thermal imagery where there is significant occlusion. Extensive testing of scenes with multiple objects at different depths and levels of occlusion shows high rates of successful registration. Ground truth experiments demonstrate the utility of disparity voting techniques for multimodal registration by yielding qualitative and quantitative results that outperform approaches that do not consider occlusions. A framework for tracking with the registered multimodal features is also presented and experimentally validated. IntroductionComputer vision applications are increasingly using multimodal imagery to obtain and process information about a scene. Specifically, the disparate yet complementary nature of visual and thermal imagery has been used in recent works to obtain additional information and robustness [1,2]. The use of both types of imagery yields information about the scene that is rich in color, depth, motion, and thermal detail. Such information can then be used to successfully detect, track, and analyze people and objects in the scene.To associate the information from each modality, corresponding data in each image must be successfully registered. In long-range surveillance applications [2], the cameras are assumed to be oriented in such a way that a global alignment R.I. Hammoud (ed.), Augmented Vision Perception in Infrared: Algorithms and 321

show abstract

Background Modelling in Infrared and Visible Spectrum Video for People Tracking

Cited by 19 publications

References 9 publications

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Sensor fusion for intelligent behavior on small unmanned ground vehicles

Registering Multimodal Imagery with Occluding Objects Using Mutual Information: Application to Stereo Tracking of Humans

Contact Info

Product

Resources

About