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检索条件"任意字段=Conference on Pattern Recognition and Tracking XXIX"

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Parallax rectification and stabilization technique for multiple objects tracking in wide area surveillance system 29

Parallax rectification and stabilization technique for multi...

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conference on pattern recognition and tracking xxix

作者： Swamidoss, Issacniwas Sayadi, Slim Al Hemeiri, Maya Khalifa Univ ETIC POB 127788 Abu Dhabi U Arab Emirates

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

tracking a moving object in a video sequence is a critical problem in wide area surveillance system applications. Our proposed system provides high-resolution motion imagery surveillance with a low frame rate on a city-sized region within which multiple moving objects are tracked simultaneously in real time. The system faces multiple challenges, including significant camera motion, strong parallax, tracking many moving objects with few pixels for each target, single-channel data, and low video frame rate. In this work, we propose a new method for parallax rectification and stabilization using a wavelet based scale-invariant feature transform (SIFT) flow technique. The results were compared with the existing state of the art methods. Various challenges associated with the detection and tracking of multiple objects in wide area surveillance systems are discussed.

关键词： Parallax distortion Wavelet transform Wide area surveillance Stitching Stabilization and tracking

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ASIFT based recognition of Fixed Shape Moving Objects and tracking via Modified Particle Filters 29

ASIFT based recognition of Fixed Shape Moving Objects and Tr...

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conference on pattern recognition and tracking xxix

作者： Masood, Haris Rehman, Saad Khan, Aimal Haider, Wasi Young, Rupert Birch, Phil Alam, Mohammad Natl Univ Sci & Technol Dept Comp Engn Islamabad Pakistan Univ Sussex Ind Informat & Signal Proc Res Grp Brighton E Sussex England Texas A&M Univ Kingsville TX USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

Object recognition and tracking are one of the key research areas in image processing and computer vision. This paper presents a novel technique which efficiently recognizes an object based on full boundary detection using affine scale invariant feature transform method (ASIFT). ASIFT is an improvement to SIFT algorithm as it provides invariance up to six parameters longitude and latitude wise. The six parameters are based on translation (2 parameters), rotation, camera axis orientation (2 parameters) and zoom. Key points commonly referred to as feature points are then obtained using the mentioned parameters which will recognize the object efficiently. Furthermore a region merging technique is used for object recognition and detection in the remote scene environment using ASIFT technique. A short pictorial comparison between SIFT and ASIFT will also be presented based on feature points calculation. After the recognition using ASIFT is performed, an algorithm will be presented for tracking of the recognized object using modified particle filter. The particle filter will use a proximal gradient (PG) approach for tracking of the recognized object in subsequent images. In case an object drastically varies its position w.r.t any of the six parameters mentioned above, ASIFT will again be called for object recognition.

关键词： Object recognition ASIFT SIFT Object tracking Particle Filters

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Stochastic gradient descent implementation of the modified forward-backward linear prediction 29

Stochastic gradient descent implementation of the modified f...

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conference on pattern recognition and tracking xxix

作者： Riasati, Vahid R. Schuetterle, Patrick G. O'Hara, Christopher Calif State Univ Northridge Elect & Comp Engn Dept Northridge CA 91325 USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

The Modified Forward Backward Linear Prediction, MFBLP, is an effective method for data dimensionality reduction and combined with eigen-vector and eigen-value techniques significant improvements in signal isolation have been shown and discussed in previous notes of this technique. In the present work, a Stochastic Gradient Descent technique is utilized to limit the dimensionality reduction of the MFBLP and the results of this technique is compared in relation to an application of the eigen-vector eigen-value technique to limit the dimensionality reduction of the MFBLP. By using a correlation metric we are able to discuss the measure of goodness of the new implementation of the MFBLP, discuss its potential, and some of its applications in this analysis. The processing approach is for active sensor systems and discussed for comparison.

关键词： Data modeling Target detection Signal to noise ratio Stochastic processes Filtering (signal processing) Signal processing Data processing Interference (communication) Linear filtering Detection and tracking algorithms

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Benchmarking Deep Learning Trackers on Aerial Videos 29

Benchmarking Deep Learning Trackers on Aerial Videos

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conference on pattern recognition and tracking xxix

作者： Minnehan, Breton Salmin, Anthony Salva, Karl Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA US Air Force Dayton OH USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

In this paper, we benchmark five state-of-the-art trackers on aerial platform videos: Multi-domain Convolutional Neural Network (MDNET) tracker, which was the winner of the VOT2015 tracking challenge, the Fully Convolutional Neural network Tracker (FCNT), the Spatially Regularized Correlation Filter (SRDCF) tracker, the Continuous Convolution Operator Tracker (CCOT) tracker, which was the winner of the VOT2016 challenge, and the Tree structure Convolutional Neural Network (TCNN) tracker. We assess performance in terms of both tracking accuracy and processing speed based on two sets of videos: a subset of the OTB dataset where the cameras are located at a high vantage point and a new dataset of aerial videos captured by a moving platform. Our results indicate that these trackers performed as expected for the videos in the OTB subset, however, tracker performance degraded significantly in aerial videos due to target size, camera motion and target occlusions. The CCOT tracker yielded the best overall performance in terms of accuracy, while the SRDCF tracker was the fastest.

关键词： Visual tracking Deep Learning MDNET FCNT SRDCF CCOT TCNN

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Vehicle tracking in full motion video using the progressively expanded neural network (PENNet) tracker 29

Vehicle tracking in full motion video using the progressivel...

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conference on pattern recognition and tracking xxix

作者： Krieger, Evan Aspiras, Theus Asari, Vijayan K. Krucki, Kevin Wauligman, Bryce Diskin, Yakov Salva, Karl Univ Dayton 300 Coll Pk Dayton OH 45469 USA MZA Associates Corp 1360 Technol CourtSuite 200 Dayton OH 45430 USA AFRL Sensors Directorate 2241 Avion Circle Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

Object trackers for full-motion-video (FMV) need to handle object occlusions (partial and short-term full), rotation, scaling, illumination changes, complex background variations, and perspective variations. Unlike traditional deep learning trackers that require extensive training time, the proposed Progressively Expanded Neural Network (PENNet) tracker methodology will utilize a modified variant of the extreme learning machine, which encompasses polynomial expansion and state preserving methodologies. This reduces the training time significantly for online training of the object. The proposed algorithm is evaluated on the DAPRA Video Verification of Identity (VIVID) dataset, wherein the selected high value -targets (HVTs) are vehicles.

关键词： tracking Full Motion Video (FMV) real-time learning high-value-targets (HVTs) extreme learning machine (ELM) Progressively Expanded Neural Network (PENNet) Aerial ISR

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Comparative Study of Local Binary pattern and Its Shifted Variant for Osteoporosis Identification 29

Comparative Study of Local Binary Pattern and Its Shifted Va...

引用

conference on pattern recognition and tracking xxix

作者： Ajmal, Hina Rehman, Saad Hussain, Farhan Abbas, Muhammad Khan, Aimal Young, Rupert Alam, Muhammad S. Natl Univ Sci & Technol Islamabad Pakistan Univ Sussex Dept Engn & Design Brighton E Sussex England Texas A&M Univ Kingsville TX USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

Osteoporosis is an age-based disease causing skeletal disorder. It is described by the little bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy subjects and osteoporotic cases show a high grade of resemblance. This study presents an evaluation of osteoporosis identification using texture descriptor Local Binary pattern (LBP) and Shift Local Binary pattern (SLBP). In contrast with the conventional LBP, with the shifted LBP specific number of binary local codes are generated for each pixel place. The distinguishing ability of the texture descriptors is evaluated using ten-fold cross validation and leave-one out scheme using different machine learning techniques. The results prove the SLBP outperforms the traditional LBP for bone texture characterization.

关键词： Texture classification Bone Radiographs Osteoporosis Shift Local Binary pattern

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Nanosensor Network for 3D+T Motion Analysis 29

Nanosensor Network for 3D+T Motion Analysis

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conference on pattern recognition and tracking xxix

作者： Leduc, Jean-Pierre Reliance Core Consulting 2121 Eisenhower AveSuite 200 Alexandria VA 22314 USA

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

This paper addresses an algorithm for motion analysis performed from digital data captured from some video cameras and from a network of motion sensors scattered over a field of interest where the 3D-FT motion analysis is performed. The paper investigates some challenges of this scheme especially the photodetection sensors. Video cameras are assigned to catch high resolution images or videos with densely and regularly distributed sensors to perform pattern classification and recognition. Motion sensors are assigned to catch motion with high sensitive sparsely distributed sensors and to build the trajectories. Motion analysis is performed as a dual control referring to both an accurate model based on theoretical mechanics and an adaptive learning system based on a supervised neural network. Motion analysis is supported by a model based on the actual mechanics that takes place on the field and the electromagnetic waves as captured by the motion sensors. The model is related to an algebraic group, called the Galilei group, that governs the motion transformations taking place in the field and the propagation of the electromagnetic waves transduced by the motion sensors into electrical signals. The Galilei group has representations in the Hilbert space of captured signals that are expressed as frames. Frames in an inner product space is a generalization of a basis in a vector space. Frames provide redundant bases and a stable way to represent and analyze digital signals. The paper investigates the major challenge of this scheme which is to describe the difference between the motion sensors and the video sensors.

关键词： Motion Analysis Kinematical Parameters Trajectories Nanosensors photodetectors Group Representations motion detection motion tracking Q-Learning

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Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment 29

Comparison of SIFT and ASIFT based filters for better Recogn...

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conference on pattern recognition and tracking xxix

作者： Masood, Haris Rehman, Saad Chaudry, Qaiser Riaz, Farhan Hassan, Ali Farooq, Umar Natl Univ Sci & Technol Dept Comp Engn Islamabad Pakistan

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a troublesome task especially while tracking the object over successive frames. Numerous methods have been proposed over the years for efficient detection of object of interest in a remote scene environment while in he meanwhile discarding all those which aren't of interest and thus considered as noise. It is still one of the most actively researched areas in the field of image processing and computer vision. In this paper, a method is proposed which will not only detect a fixed shape object in a remote scene environment but it will also track it over successive frames. However, an additional methodology is also proposed which will detect the object in case of change of viewing angles e.g. scenario's like rotation of object, zooming etc. First, Scale Invariant Feature Transfoiin (SIFT) will be presented which will provide invariance up to four different parameters i.e. rotation, translation and zoom. In the second phase, ASIFT will be used which will provide invariance up to six different parameters i.e. translation, rotation, zoom and camera axis orientations. After both algorithms are presented, a detailed comparison between both is presented. Detection of object is performed with the help of both SIFT and ASIFT and then comparison is made based on feature points. Finally, tracking is performed based on Proximal Gradient Particle filters which will further strengthen the comparison between SIFT and ASIFT once the object that needs to be tracked changes its course of motion or zoom. Experimental results will show which one of the two filters is more efficient.

关键词： Object recognition ASIFT SIFT Object tracking Particle Filters

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Energy criterion in correlation-based face recognition applications 29

Energy criterion in correlation-based face recognition appli...

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conference on pattern recognition and tracking xxix

作者： Kouas, A. Elbouz, M. Ouerhani, Y. Alfalou, A. Brosseau, C. Fakhfakh, A. Ghozzi, F. Equipe Vis Smart Autonomous Syst SAS Lab 20 Rue Cuirasse Bretagne CS 42807 F-29228 Brest 2 France Actris 24 Rue Victor GrignardBP 30143 F-29803 Brest 9 France Univ Brest Lab STICC CS 938376 Ave Le Gorgeu F-29238 Brest 3 France ENET Sfax Dept Elect Sfax Tunisia

ISBN: (数字)9781510618107

ISBN: (纸本)9781510618107

Today, communications security, i.e. the discipline of preventing unauthorized interceptors from accessing telecommunications in an intelligible form, while still delivering content to the intended recipients, is a main issue in our modern society especially. In this paper, attention is drawn to the importance and relevance of optical correlation techniques for detection and tracking people. In order to be efficient, these techniques need pre- or post-processing steps to take into account the environmental conditions. The aim of this work is to improve the performance of the optical correlation method, based on a new decision process in order to reduce the false detection rate. To realize this, we propose a method using a VanderLugt correlator with a phase-only filter for face recognition using two criteria for decision making based on the values of the peak-to-correlation energy and the energy distribution in different parts of the correlation plane. In the three-step algorithm, the first stage consists by dividing the correlation plane into nine equal sub-planes. In the second stage the energy of each sub-plane is computed, while in the last stage the classification criterion is realized and the recognition rate is calculated. Numerous tests were performed using the Pointing Head Pose Image Database. They show the effectiveness of the method in terms of face recognition detection rate without pre-processing phase and with 0% false detection.

关键词： Facial recognition systems Databases Buildings Defense and security Fuzzy logic Head Optical correlators Optical tracking Phase only filters Video surveillance

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Automatic Machine Learning for Target recognition 29

Automatic Machine Learning for Target Recognition

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SPIE's conference on Automatic Target recognition xxix

作者： Blasch, Erik Majumder, Uttam K. Rovito, Todd Zulch, Peter Velten, Vince Air Force Res Lab AFOSR Arlington VA 22203 USA Air Force Res Lab Informat Directorate Rome NY 13441 USA Air Force Res Lab Sensors Directorate Wright Patterson AFB OH USA

ISBN: (纸本)9781510626423

Automatic Target recognition (ATR) seeks to improve upon techniques from signal processing, pattern recognition (PR), and information fusion. Currently, there is interest to extend traditional ATR methods by employing Artificial Intelligence (AI) and Machine Learning (ML). In support of current opportunities, the paper discusses a methodology entitled: Systems Experimentation efficiency effectives Evaluation Networks (SEeeEN). ATR differs from PR in that ATR is a system deployment leveraging pattern recognition (PR) in a networked environment for mission decision making, while PR/ML is a statistical representation of patterns for classification. ATR analysis has long been part of the COMPrehensive Assessment of Sensor Exploitation (COMPASE) Center utilizing measures of performance (e.g., efficiency) and measures of effectiveness (e.g., robustness) for ATR evaluation. The paper highlights available multimodal data sets for Automated ML Target recognition (AMLTR).

关键词： Artificial Intelligence Multimodal Deep Learning Deep Neural Networks Context-enhanced information fusion situation assessment probabilistic models target-tracking and recognition temporal networks

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