189 research outputs found
Object tracking under illumination variations using 2D-cepstrum characteristics of the target
Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In this paper, a 2D-cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target object provides robustness to varying illumination conditions. Another contribution of the paper is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. ©2010 IEEE
Illumination Condition Effect on Object Tracking: A Review
Illumination is an important concept in computer science application. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities and at least six more aspects. By using the review approach, our tracker is able to adapt to irregular illumination variations and abrupt changes of brightness. In static environment segmentation of object is not complex. In dynamic environment due to dynamic environmental conditions such as waving tree branches, shadows and illumination changes in the wind object segmentation is a difficult and major problem that needs to be handled well for a robust surveillance system. In this paper, we survey various tracking algorithms under changing lighting condition
Video processing methods robust to illumination variations
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 43-46.Moving shadows constitute problems in various applications such as image segmentation,
smoke detection and object tracking. Main cause of these problems
is the misclassification of the shadow pixels as target pixels. Therefore, the use
of an accurate and reliable shadow detection method is essential to realize intelligent
video processing applications. In the first part of the thesis, a cepstrum
based method for moving shadow detection is presented. The proposed method is
tested on outdoor and indoor video sequences using well-known benchmark test
sets. To show the improvements over previous approaches, quantitative metrics
are introduced and comparisons based on these metrics are made.
Most video processing applications require object tracking as it is the base operation
for real-time implementations such as surveillance, monitoring and video
compression. Therefore, accurate tracking of an object under varying scene and
illumination conditions is crucial for robustness. It is well known that illumination
variations on the observed scene and target are an obstacle against robust
object tracking causing the tracker lose the target. In the second part of the
thesis, a two dimensional (2D) cepstrum based approach is proposed to overcome
this problem. Cepstral domain features extracted from the target region
are introduced into the covariance tracking algorithm and it is experimentally
observed that 2D-cepstrum analysis of the target region provides robustness to
varying illumination conditions. Another contribution is the development of the
co-difference matrix based object tracking instead of the recently introduced covariance
matrix based method.
One of the problems with most target tracking methods is that they do not
have a well-established control mechanism for target loss which usually occur when illumination conditions suddenly change. In the final part of the thesis, a
confidence interval based statistical method is developed for target loss detection.
Upper and lower bound functions on the cumulative density function (cdf) of the
target feature vector are estimated for a given confidence level. Whenever the
estimated cdf of the detected region exceeds the bounds it means that the target
is no longer tracked by the tracking algorithm. The method is applicable to most
tracking algorithms using features of the target image region.Çoğun, FuatM.S
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges
Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed
Recent Trends in Computational Intelligence
Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications
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