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Comparison of skin color detection and tracking methods under varying illumination -: art. no. 043014

JOURNAL OF ELECTRONIC IMAGING 2005年第4期14卷

作者： Martinkauppi, B Soriano, M Pietikäinen, M Univ Oulu Dept Elect & Informat Engn FIN-90014 Oulu Finland Univ Philippines Natl Inst Phys Quezon City Philippines

When skin areas such as faces and hands are imaged under natural environments their color appearance is frequently affected by variations in illumination intensity and chromaticity. In color-based skin tracking and detection, changing intensity is often dismissed either with the use of normalized, intensity-invariant color coordinates or by additionally modeling possible skin intensities. Chromaticity variations are rarely considered, although they are common in practice. In most approaches considering chromaticity, the experiments are done with a small or undefined variation range. It is difficult to compare different approaches and assess their applicability range for this reason. To improve the situation, we evaluate the performance of four state-of-the-art methods under drastic but practically common illumination changes. The effect of illumination chromaticity for skin is clearly defined, and based on it we draw conclusion about the performance of these approaches. (c) 2005 SPIE and IS&T.

关键词： Skin Cameras Video Image segmentation RGB color model Calibration detection and tracking algorithms Light sources and illumination Data modeling Image filtering

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Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

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JOURNAL OF ELECTRONIC IMAGING 2004年第4期13卷 802-813页

作者： Braga-Neto, U Choudhary, M Goutsias, J Univ Texas MD Anderson Canc Ctr Sect Clin Canc Genet Houston TX 77030 USA Texas A&M Univ Dept Elect Engn College Stn TX 77840 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA

We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/ transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method. (C) 2004 SPIE and IST.

关键词： Target detection detection and tracking algorithms Forward looking infrared Binary data Image filtering Automatic tracking Sensors Video Image enhancement Image processing

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Multi-attitude based target localization method

Multi-attitude based target localization method

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Yan, Suqing Xu, Xiaoyue Xiao, Jianming Ji, Yuanfa Hawari Bin Ghazali, Kamarul Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Science and Engineering Guilin University Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Centre for Advanced Industrial Technology University of Malaysia Pahang Al Sultan Abdullah Pekan Campus Pahang Pekan Malaysia

ISBN: (纸本)9781510689176

In recent years, location-based services have gradually penetrated all aspects of people's daily life, bringing great convenience to people's work and life. Pedestrian Dead Reckoning (PDR) is a positioning method that can derive a pedestrian's movement trajectory by relying on sensors information, with a high degree of autonomy and continuity. However, existing PDR tends to restrict the target posture to remain steadily pointing in the direction of pedestrian movement throughout the localization process. In addition, although it can achieve a high degree of positioning accuracy in a short period of time, it generates a cumulative error over time. To these problems, this paper proposes a fusion localization method that combines PDR based on multiple smartphones pose recognition and acoustic localization. In this method, we first employ a GWO-ELM based target posture recognition model. Then based on the posture recognition result for PDR localization, Time Difference of Arrival (TDOA) technique is used for acoustic localization. Finally, particle filtering is utilized to combine the two methods. To demonstrate the validity of the method, we performed several experiments on a motion trajectory with a length of 97.2 meters. The results show that the method has better localization performance than the traditional method. In addition, it can realize high-precision localization under different devices with universality and stability. © 2025 SPIE.

关键词： Acoustics Target recognition detection and tracking algorithms Particles Sensors Education and training Gesture recognition Satellite navigation systems Tunable filters Machine learning

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A smart camera application: DSP-based people detection and tracking

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JOURNAL OF ELECTRONIC IMAGING 2000年第3期9卷 336-346页

作者： Cheng, V Kehtarnavaz, N Texas A&M Univ Dept Elect Engn DSP Lab College Stn TX 77843 USA

As an application of smart cameras, this paper presents an algorithm and its real-time implementation for detecting and tracking multiple people moving in a surveillance scene. The algorithm is devised to work for low resolution image frames and any camera angle. First an adaptive background subtraction module is used to obtain potential moving target areas. Second a human body detection module is activated to locate the heads. Third a multitarget tracking module is deployed to resolve ambiguities associated with crosspaths and merged targets. The algorithm is implemented an the Texas instruments TMS320C6201 fixed-point DSP processor for real-time processing of video signals. This real-lime capability allows field deployment for the purpose of collecting surveillance data such as the number of people and their crossing frequency in a certain area. (C) 2000 SPIE and IS&T. [S1017-9909(00)01802-X].

关键词： Digital signal processing Cameras detection and tracking algorithms Head Calibration Target detection Video Image resolution Optical tracking Surveillance

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Distorted IR target detection and tracking using composite filters

Distorted IR target detection and tracking using composite f...

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Conference on Photonic Devices and algorithms for Computing V

作者： Iftekharuddin, KM Shaik, JS Univ Memphis Intelligent Syst & Image Proc ISIP Lab Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)081945074X

The automatic target recognition (ATR), often time, is limited by the presence of background clutter and distortions such as scale, translation and rotation (both in-plane and out-of-plane) in both single and multi object cases. Such distortion invariant ATR and image understanding have been the subject of intense research in machine vision. In a previous work, we have demonstrated the usefulness of an amplitude-coupled minimum-average correlation energy (AC-MACE) filter in in-plane rotated SAR image ATR. The AC-MACE filter outperforms the regular MACE filter in rotation-related cases. Motion tracking is also an important task in computer vision, especially, when objects are subjected to certain viewing transformation. There are many problems in which very small objects undergoing motion must be detected and then tracked. For example, one of the most difficult goals of ATR is to spot incoming objects at long range, wherein the motion appears small and the signal to noise ratio (SNR) is poor. The system must be able to track such targets long enough to identify whether the object is a friend or foe. In this work, we are interested in locating both long-range and short-range moving objects in IR images wherein the object may vary from a few pixels in size to a large number of pixels in a sequence of IR images. The targets are submerged in background noise and clutter. Additionally, the tracking problem also involves out-of-plane rotation of the target. Thus, we investigate both MACE and AC-MACE filter for rotation and size invariant target detection and tracking using realistic IR images.

关键词： detection and tracking algorithms Image filtering Optical filters Automatic target recognition Electronic filtering Infrared imaging Nonlinear filtering Phase only filters Composites Target detection

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Road detection and tracking for autonomous mobile robots

Road detection and tracking for autonomous mobile robots

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Conference on Unmanned Ground Vehicle Technology IV

作者： Hong, TH Rasmussen, C Chang, T Shneier, M Natl Inst Stand & Technol Intelligent Syst Div Gaithersburg MD 20899 USA

ISBN: (纸本)0819444650

As part of the Army's Demo III project, a sensor-based system has been developed to identify roads and to enable a mobile robot to drive along them. A ladar sensor, which produces range images, and a color camera are used in conjunction to locate the road surface and its boundaries. Sensing is used to constantly update an internal world model of the road surface. The world model is used to predict the future position of the road and to focus the attention of the sensors on the relevant regions in their respective images. The world model also determines the most suitable algorithm for locating and tracking road features in the images based on the current task and sensing information. The planner uses information from the world model to determine the best path for the vehicle along the road. Several different algorithms have been developed and tested on a diverse set of road sequences. The road types include some paved roads with lanes, but most of the sequences are of unpaved roads, including dirt and gravel roads. The algorithms compute various features of the road images including smoothness in the world model map and in the range domain, and color features and texture in the color domain. Performance in road detection and tracking are described and examples are shown of the system in action.

关键词： Roads Sensors LIDAR detection and tracking algorithms Cameras Image segmentation Data modeling Sensory processes Mobile robots Algorithm development

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Robust Human detection, tracking, and Recognition in Crowded Urban Areas

Robust Human Detection, Tracking, and Recognition in Crowded...

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5th Conference on Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR as part of the SPIE Defense and Security Sensing Symposium

作者： Chen, Hai-Wen McGurr, Mike Booz Allen Hamilton Inc Belcamp MD 21017 USA

ISBN: (纸本)9781628410167

In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection. The human tracking process approach includes: 1) Color and intensity feature matching track candidate selection;2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively;3) Adaptive track gate size selection for reducing false tracking probability;and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with 'tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google-Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more tha

关键词： Cameras Target detection Image processing Feature extraction Image segmentation Biometrics detection and tracking algorithms Relays Target recognition Automatic tracking

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Occlusion and split detection and correction for object tracking in surveillance applications

Occlusion and split detection and correction for object trac...

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Conference on Visual Communications and Image Processing 2007

作者： Vazqueza, Carlos Ghazal, Mohammed Amer, Aishy Adv Video Syt Commun Res Ctr Ottawa ON Canada Concordia Univ Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9780819466211

This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in feature-based tracking of objects for surveillance applications. The proposed algorithm detects sudden variations of spatio-temporal features of objects in order to identify possible occlusion or split events. The detection is followed by a validation stage that uses past tracking information to prevent false detection of occlusion or split. Special care is taken in case of heavy occlusion, when there is a large superposition of objects. In this case the system relies on long-term 'temporal behavior of objects to avoid updating the video object features with unreliable (e.g. shape and motion) information. Occlusion is corrected by separating occluded objects. For the detection of splits, in addition to the analysis of spatio-temporal changes in objects features, our algorithm analyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects, such as in the deposit of an object. Split is corrected by physically merging the objects detected to be split. To validate the proposed approach, objective and visual results are presented. Experimental results show the ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposed algorithm is most suitable in video surveillance applications due to: its good performance in multiple, heavy, and total occlusion;its distinction between real object separation and faulty object split;its handling of simultaneous occlusion and split events;and its low computational complexity.

关键词： Video Information operations detection and tracking algorithms Image segmentation Surveillance Video surveillance Image processing Error analysis Visualization Image processing algorithms and systems

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A robust real-time object detection and tracking system

A robust real-time object detection and tracking system

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Conference on Acquisition, tracking, Pointing and Laser Systems Technologies XXII

作者： Yue, Zhanfeng Narasimha, Pramod Lakshmi Topiwala, Pankaj FastVDO Inc Columbia MD 21044 USA

ISBN: (纸本)9780819471628

We propose a real-time vehicle detection and tracking system from an electro-optical (EO) surveillance camera. Real-time object detection remains a challenging computer vision problem in uncontrolled environments. The state-of-the-art adaboosting technique [2] is used to serve as a robust object detector. In addition to the generally-used Haar features, we propose to include comer features to improve the detection performance of the vehicles. Having the object of interest detected, we use the detection results to initialize the object tracking module. We propose an advanced, adaptive particle-filtering based algorithm to robustly track the moving target by adaptively changing the appearance model of the target. We use the affine transformation to describe the motion of the object across frames. By drawing multiple particles on the transformation parameters, our approach provides high performance while facilitating implementation of this algorithm in hardware with parallel processing capability. In order to resume from the lost track case, which may result from the object's out of boundary or being occluded, we utilize the prior information (height-to-width ratio) and the temporal information of the object to estimate if the tracking is reliable. Object detectors will be evoked at the frames which fail in tracking the objects reliably. We also check for occlusion by comparing hue values within the rectangular region for the current frame against that of the previous frame. detection is re-initialized for the next frame if an occlusion is claimed for the current frame. The system works very well in terms of speed and performance for the real surveillance video.

关键词： detection and tracking algorithms Video Sensors Feature extraction Particles Surveillance Information visualization Solids Visualization Electro optical modeling

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Close to real-time robust pedestrian detection and tracking

Close to real-time robust pedestrian detection and tracking

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Conference on Video Surveillance and Transportation Imaging Applications

作者： Lipetski, Y. Loibner, G. Sidla, O. SLR Engn GmbH Graz Austria

ISBN: (纸本)9781628414974

Fully automated video based pedestrian detection and tracking is a challenging task with many practical and important applications. We present our work aimed to allow robust and simultaneously close to real-time tracking of pedestrians. The presented approach is stable to occlusions, lighting conditions and is generalized to be applied on arbitrary video data. The core tracking approach is built upon tracking-by-detections principle. We describe our cascaded HOG detector with successive CNN verification in detail. For the tracking and re-identification task, we did an extensive analysis of appearance based features as well as their combinations. The tracker was tested on many hours of video data for different scenarios;the results are presented and discussed.

关键词： Sensors Video Switches Video surveillance Optical tracking detection and tracking algorithms Cameras Automatic tracking Light sources and illumination Visualization

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