BLOB TRACKING MODEL USING VIDEO OBJECT TRACKING IN THE COMPRESSED DOMAIN

S.Rajasekaran#1,

#1II-M.E, Department of VLSI DESIGN, Sasuire college of engineering and technology, vijayamangalam. rajasekar0107@gmail.com

N.Kannapiran M.E., *2 Asst professor, Department of ECE, , Sasuire college of engineering and

technology, vijayamangalam.

ABSTRACT Despite the recent progress in both

pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (STMRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object’s motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bit stream to perform tracking. First, the MVs are pre processed through intra coded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object’s motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.

INTRODUCTION Object tracking can be defined as the

process of segmenting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. Object tracking in video processing follows the segmentation step and is more or less equivalent to the „recognition‟ step in the image processing. Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications, including traffic monitoring, automated remote video surveillance, and people tracking. There are basically three approaches in object tracking. Feature-based methods aim at extracting characteristics such as points, line segments from image sequences, tracking stage is then ensured by a matching procedure at every time instant. Differential methods are based on the optical flow computation, i.e. on the apparent motion in image sequences, under some regularization assumptions. The third class uses the correlations to measure inter image displacements. Selection of a particular approach largely depends on the domain of the problem.

EXISTING SYSTEM ANALYSIS: From the discussion, in the existing

paper it can be seen that object tracking has many useful applications in the robotics and computer vision fields. Several researchers have explored and implemented different approaches for tracking. The success of a particular approach depends largely on the problem domain.

In other words, a method that is successful in robot navigation may not be equally successful in automated surveillance. Further there exists a cost or performance trade off. For real time applications we may need a fast high performance system on the other hand offline applications we may use a relatively cheap (and slower in performance). It can also be seen from the diverse nature of the techniques used that the field has a lot of room for improvement. Differential methods are based on the optical flow computation, i.e. on the apparent motion in image sequences, under some regularization assumptions. The third class uses the correlation to measure inters image displacements. Selection of a particular approach largely depends on the domain of Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Video tracking can be a time consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use object recognition techniques for tracking. Object tracking in video processing follows the segmentation step and is more or less equivalent to the recognition step in the image processing. Detection of moving objects in video streams is the first relevant step of

information extraction in many computer vision applications.

Moving object segmentation in compressed domain plays an important role in many real-time applications, e.g. video indexing, video transcending, video surveillance, etc. Because H.264/AVC is the up-to-date video-coding standard, few literatures have been reported in the area of video analysis on H.264/AVC compressed video.In contrast to prior video coding standards, DC coefficients in intra-coded pictures no longer represent average energy. But only represent an energy difference. Algorithms working on DC coefficients can therefore no longer be applied to H.264/AVC bit streams. Furthermore H.264/AVC supports variable block size motion compensation. A macro block can now be partitioned into several smaller blocks, where each block has its own MVs. This is very different from former MPEG video standards, where regular block size MVs are used .we present a new object detection algorithm working on H.264/AVC which relies on MVs. In order to alleviate the noisy motion vector field and, as a consequence, to make the detection more accurate, the reliability of the MVs is estimated and incorporated during the

detection. A new algorithm for moving object detection in the H.264/AVC compressed domain. By estimating the reliability of the MVs based on surrounding frames noisy MVs are removed, while MVs corresponding to true motion are preserved.

Future work includes optimal selection of variable thresholds based on a background model. Furthermore, to make the detection results more accurate, DCT coefficients will be considered as well.

PROPOSED SYSTEM

In this proposed paper, we have presented a novel approach to track a moving object in a H.264/AVC-compressed video. The only data from the compressed stream used in the proposed method are the motion vectors and block coding modes. As a result, the proposed method has a fairly low processing time, yet still provides high accuracy. After the pre processing stage, which consists of intra-coded block motion approximation and global motion compensation, we employ Spatio-Temporal Markov Random Field model to detect and track a moving target. Using this model, an estimate of the labeling of the current frame is formed based on the previous frame labelling and current motion information. The results of experimental evaluations on ground truth video demonstrate superior functionality and accuracy of our approach against other state of the art compressed-domain segmentation/tracking approaches.

Although our algorithm works well even with fixed parameter values, possibly better performance may be obtained by adaptive tuning, although this would in general increase the complexity .Before comparing results from SMRF and STMRF, we did a simple test to show that the quality of depth maps from SMRF method is influenced by the Signal to Noise Ratio (SNR) of the TOF sensor. In one of our

previous work in fusing stereo with TOF sensors, we focused on static scenes and took several depth maps and compute the mean. The result shows that it is less susceptible to image noise. The mean depth map was used as input. In Figure 6, we visualize 3D points and depth maps using one shot (low SNR) and the averaged of 10 shots (high SNR).

EVALUATION

We can clearly see the depth map from multiple shots is better than that from the single shot. However, simple average over multiple shots can be applied only to static scenes. Our STMRF method uses temporal correspondences to reduce noises and improve depth estimate in dynamic scenes. In this way, it can also be viewed as a temporal denoising scheme when applying to the TOF sensor alone. We evaluate our STMRF method on a number of real scenes. For each scene, we compare depth maps and their geometrical representations (3D point clouds). All of them are using the left camera as the reference view.

To make a fair comparison, we set

the message truncation value Tm = 0:3, and we set the TOF sensor‟s capturing frequency around 20 FPS and the stereo rig around 100 FPS. All compared frames are using the TOF sensor‟s time stamp as the reference. The system specification includes the Software requirements for the simulation of the project. The project

is simulated with the help of a Desktop Computer or a Laptop. The development tool used for the simulation of this project is MATLAB 7.0

CONCLUSION

To detect moving objects within a sequence of video frames, we construct a MRF model such that each pixel‟s hidden state (motion likelihood) has a 6-connected spatio-temporal neighborhood. a novel approach to track a moving object in a H.264/AVC-compressed video. The only data from the compressed stream used in the proposed method are the motion vectors and block coding modes. As a result, the proposed method has a fairly low processing time, yet still provides high accuracy.After the preprocessing stage, which consists of intra-coded block motion approximation and global motion compensation, we employ Spatio-Temporal Markov Random Field model to detect and track a moving target. Using this model, an estimate of the labeling of the current frame is formed based on the previous frame labeling and current motion information.The results of experimental evaluations on ground truth video demonstrate superior functionality and accuracy of our approach against other state-of the- art compressed-domain segmentation/tracking approaches.

FUTURE ENHANCEMENT In this paper, the 3D MRF-based

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