Browsing by Subject "Video compression."

Now showing 1 - 9 of 9

Open Access
CUDA based implementation of flame detection algorithms in day and infrared camera videos
(2011) Hamzaçebi, Hasan
Automatic fire detection in videos is an important task but it is a challenging problem. Video based high performance fire detection algorithms are important for the detection of forest fires. The usage area of fire detection algorithms can further be extended to the places like state and heritage buildings, in which surveillance cameras are installed. In uncontrolled fires, early detection is crucial to extinguish the fire immediately. However, most of the current fire detection algorithms either suffer from high false alarm rates or low detection rates due to the optimization constraints for real-time performance. This problem is also aggravated by the high computational complexity in large areas, where multicamera surveillance is required. In this study, our aim is to speed up the existing color video fire detection algorithms by implementing in CUDA, which uses the parallel computational power of Graphics Processing Units (GPU). Our method does not only speed up the existing algorithms but it can also reduce the optimization constraints for real-time performance to increase detection probability without affecting false alarm rates. In addition, we have studied several methods that detect flames in infrared video and proposed an improvement for the algorithm to decrease the false alarm rate and increase the detection rate of the fire.
Open Access
Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection
(2009) Günay, Osman
Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.
Open Access
Fire and flame detection methods in images and videos
(2010) Habiboğlu, Yusuf Hakan
In this thesis, automatic fire detection methods are studied in color domain, spatial domain and temporal domain. We first investigated fire and flame colors of pixels. Chromatic Model, Fisher’s linear discriminant, Gaussian mixture color model and artificial neural networks are implemented and tested for flame color modeling. For images a system that extracts patches and classifies them using textural features is proposed. Performance of this system is given according to different thresholds and different features. A real-time detection system that uses information in color, spatial and temporal domains is proposed for videos. This system, which is develop by modifying previously implemented systems, divides video into spatiotemporal blocks and uses features extracted from these blocks to detect fire.
Open Access
Object-based 3-d motion and structure analysis for video coding applications
(1997) Alatan, A. Aydin
Novel 3-D motion analysis tools, which can be used in object-based video codecs, are proposed. In these tools, the movements of the objects, which are observed through 2-D video frames, are modeled in 3-D space. Segmentation of 2-D frames into objects and 2-D dense motion vectors for each object are necessary as inputs for the proposed 3-D analysis. 2-D motion-based object segmentation is obtained by Gibbs formulation; the initialization is achieved by using a fast graph-theory based region segmentation algorithm which is further improved to utilize the motion information. Moreover, the same Gibbs formulation gives the needed dense 2-D motion vector field. The formulations for the 3-D motion models are given for both rigid and non- rigid moving objects. Deformable motion is modeled by a Markov random field which permits elastic relations between neighbors, whereas, rigid 3-D motion parameters are estimated using the E-matrix method. Some improvements on the E-matrix method are proposed to make this algorithm more robust to gross errors like the consequence of incorrect segmentation of 2-D correspondences between frames. Two algorithms are proposed to obtain dense depth estimates, which are robust to input errors and suitable for encoding, respectively. While the former of these two algorithms gives simply a MAP estimate, the latter uses rate-distortion theory. Finally, 3-D motion models are further utilized for occlusion detection and motion compensated temporal interpolation, and it is observed that for both applications 3-D motion models have superiority over their 2-D counterparts. Simulation results on artificial and real data show the advantages of the 3-D motion models in object-based video coding algorithms.
Open Access
Regularized motion estimation techniques and their applications to video coding
(1996) Kıranyaz, Serkan
Novel regularized motion estimation techniques and their possible applications to video coding are presented. A block matching motion estimation algorithm which extracts better block motion field by forming and ininimizing a suitable energy function is introduced. Based on ciri ¿idciptive structure onto block sizes, cui cidvcinced block matching ¿ilgorithm is presented. The block sizes are adaptively ¿idjusted according to the motion. Blockwise coarse to fine segmentation based motion estimation algorithm is introduced for further reduction on the number of bits that are spent lor the coding of the block motion vectors. Motion estiiricition algorithms which can be used lor ¿iverage motion determination and artificial frame generation by fractional motion compensation are ¿ilso developed. Finallj^, an alternative motion estimation cind compensation technique which defines feciture based motion vectors on the ob ject boundciries and reconstructs the decoded frame from the interpolation of the compensated object boundaries is presented. All the algorithms developed in this thesis are simulated on recil or synthetic images cind their performance is demonstrcited.
Open Access
Three-dimensional video coding on mobile platforms
(2009) Bal, Can
With the evolution of the wireless communication technologies and the multimedia capabilities of the mobile phones, it is expected that three-dimensional (3D) video technologies will soon get adapted to the mobile phones. This raises the problem of choosing the best 3D video representation and the most efficient coding method for the selected representation for mobile platforms. Since the latest 2D video coding standard, H.264/MPEG-4 AVC, provides better coding efficiency over its predecessors, coding methods of the most common 3D video representations are based on this standard. Among the most common 3D video representations, there are multi-view video, video plus depth, multi-view video plus depth and layered depth video. For using on mobile platforms, we selected the conventional stereo video (CSV), which is a special case of multi-view video, since it is the simplest among the available representations. To determine the best coding method for CSV, we compared the simulcast coding, multi-view coding (MVC) and mixed-resolution stereoscopic coding (MRSC) without inter-view prediction, with subjective tests using simple coding schemes. From these tests, MVC is found to provide the best visual quality for the testbed we used, but MRSC without inter-view prediction still came out to be promising for some of the test sequences and especially for low bit rates. Then we adapted the Joint Video Team’s reference multi-view decoder to run on ZOOMTM OMAP34xTM Mobile Development Kit (MDK). The first decoding performance tests on the MDK resulted with around four stereo frames per second with frame resolutions of 640×352. To further improve the performance, the decoder software is profiled and the most demanding algorithms are ported to run on the embedded DSP core. Tests resulted with performance gains ranging from 25% to 60% on the DSP core. However, due to the design of the hardware platform and the structure of the reference decoder, the time spent for the communication link between the main processing unit and the DSP core is found to be high, leaving the performance gains insignificant. For this reason, it is concluded that the reference decoder should be restructured to use this communication link as infrequently as possible in order to achieve overall performance gains by using the DSP core.
Open Access
A very low bit rate video coder decoder
(1997) Bostancı, Hakkı Tunç
A very low bit rate video coding decoding algorithm (codec) that can be used in real-time applications such as videotelephony is proposed. There are established video coding standards (MPEG-1, MPEG-2, H.261, H.263) and standards under development (MPEG-4 and MPEG-7). The proposed cod ing method is based on temporal prediction, followed by spatial prediction and finally entropy coding. The operations are performed in a color-palletized space. A new method based on parameterizing texture features is used for de termining the difference image, which shows the temporal prediction errors. A new hierarchical 2D spatial prediction scheme is proposed for lossy or lossless predictive coding of arbitrary shaped regions. A simulation of the codec is implemented in object oriented C++ language using template based program ming techniques. The codec is tested using official MPEG-4 test sequences. Its performance is compared to H.263 coding at the same bit rate and although numerically inferior, visually similar results are obtained.
Open Access
Video processing methods robust to illumination variations
(2010) Çoğun, Fuat
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.
Open Access
Web-based user interface for query specification in a video database system
(2001) Şaykol, Ediz