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Efficient Motion Tracking Algorithm for Stereo Vision System

Dheepak Mohanraj
Assistant Professor, Dept. of Electrical & Electronics Engineering, AMET University, Chennai, India.
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Abstract

This paper deals the video motion estimation and compensation is a part of the encoding technique which helps to determine the correct motion vector as well as reduce the error caused due to redundancies between successive frames in video images. This motion estimation and compensation technique is widely applicable in security systems and motion tracking systems in robotics. This paper deals with developing a efficient multiple search algorithm which can overcome the drawbacks of existing algorithms like center based diamond search algorithm and three step search algorithm. The proposed algorithm avoids useless search thereby improves computational time, speed as well as it has multiple search characteristics which helps to reduce the mean square search and give better search results thereby resulting in better motion vector detection even for a smaller motions specially employed in stereovision system.

Keywords

Block matching, Stereovision system, Motion estimation, minimum block distortion point (MBD).

INTRODUCTION

The increase demand to incorporate video data into telecommunications services, the corporate environment, in entertainment industry, in robotics and even at home has made digital video technology an essential one. A problem however is that still image and digital video rates are very large ,consume lot of bandwidth and storage space etc.For this reason ,Video compression standards have been developed to eliminate picture redundancy, allowing the video information to be transmitted and stored in a compact and efficient manner with better video quality. In general, successive pictures in a motion video sequence tend to be highly correlated, that is, the picture changes slightly over a small period of time.
This implies that the arithmetic difference between these pictures is small. For this reason, compression ratios for motion video sequences may be increased by encoding the arithmetic difference between two or more successive frames. In contrast, Objects that are in motion increase the arithmetic difference between frames which in turn implies that more bits are required to encode the sequence. To address this issue, motion estimation is utilized to determine the displacement of an object in motion. It is a process by which elements in a picture are best correlated to elements in other picture (a head or behind) by this estimated amount of motion. The amount of motion is encapsulated in the motion vector.Foreward motion vectors refer to correlation with previous pictures. Backward motion vectors refer to correlation with future pictures. An efficient motion estimation algorithm increases frames correlation, which in turn minimizes pixel arithmetic difference.

RELATED WORK

The result obtained in not only higher compression ratios but also in high quality decoded video sequence. Motion estimation is an extremely computationally intensive operation difficult to implement in real time .For this reason, a variety of motion estimation algorithm have been implemented by the industry. Various motion estimation algorithm were analysed and proposed algorithm was preferred because of its best search result, improves computational time, speed as well as it has multiple search characteristics this can be very helpful for stereo vision systems like stero vision robot for tracking the obstacles and it also used in security system.

BLOCK BASED MOTION COMPENSATION

Block based motion compensated video compression takes place in a number of distinct stages. The flow chart above illustrated below shows how the output from the earlier processes forms the input to later processes. Consequently choices made at early stages can have an impact of the effectiveness of later stages. To fully understand the issues involved with this type of video compression it is necessary to examine each of the stages in detail.
These stages can be described as
Frame Segmentation
Search Threshold
Block Matching
Sub-Optimal Block Matching Algorithms
Motion Vector Correction
Vector Coding
Prediction Error Coding
Frame Segmentation
The current frame of video to be compressed is divided into equal sized non-overlapping rectangular blocks. Block size affects the performance of compression techniques. The larger the block size, the fewer the number of blocks, and hence fewer motion vectors need to be transmitted. However, borders of moving objects do not normally coincide with the borders of blocks and so larger blocks require more correction data to be transmitted Small blocks result in a greater number of motion vectors, but each matching block is more likely to closely match its target and so less correction data is required. The block size is too small then the compression system will be very sensitive to noise Thus block size represents a trade off between minimizing the number of motion vectors and maximizing the quality of the matching blocks.
Search Threshold
If the difference between the target block and the candidate block at the same position in the past frame is below some threshold then it is assumed that no motion has taken place and a zero vector is returned. Thus the expense of a search is avoided. Most video codec’s employ a threshold in order to determine if the computational effort of a search is warranted.
Block Matching
Block matching is the most time consuming part of the encoding process. During block matching each target block of the current frame is compared with a past frame in order to find a matching block. When the current frame is reconstructed by the receiver this matching block is used as a substitute for the block from the current frame. Block matching takes place only on the luminance component of frames.
The colour components of the blocks are included when coding the frame but they are not usually used when evaluating the appropriateness of potential substitutes or candidate blocks. The search can be carried out on the entire past frame, but is usually restricted to a smaller search area centered on the position of the target block in the current frame.
This practice places an upper limit, known as the maximum displacement, on how far objects can move between frames, if they are to be coded effectively. The maximum displacement is specified as the maximum number of pixels in the horizontal and vertical directions that a candidate block can be from the position of the target block in the original frame.
The quality of the match can often be improved by interpolating pixels in the search area, effectively increasing the resolution within the search area by allowing hypothetical candidate blocks with fractional displacements.

Sub-Optimal Block Matching Algorithms

The exhaustive search is computationally very intensive and requires the distortion function to be evaluated many times for each target block to be matched. Considerable research has gone into developing block matching algorithms that find suitable matches for target blocks but require fewer evaluations. Such algorithms test only some of the candidate blocks from the search area and choose a match from this subset of blocks. Hence they are known as sub-optimal algorithms. Because they do not examine all of the candidate blocks, the choice of matching block might not be as good as that chosen by an exhaustive search.

Motion vector correction

Once the best substitute, or matching block, has been found for the target block, a motion vector is calculated. The motion vector describes the location of the matching block from the past frame with reference to the position of the target block in the current frame. Motion vectors, irrespective of how they are determined, might not correspond to the actual motion in the scene. This may be due to noise, weaknesses in the matching algorithm, or local minima. The property that is exploited in spatially dependent algorithms can be utilized after the vectors have been calculated in an attempt to correct them. Smoothing techniques can be applied to the motion vectors that can detect erratic vectors and suggest alternatives.

Vector coding

Once determined, motion vectors must be assigned bit sequences to represent them. Because so much of the compressed data will consist of motion vectors, the efficiency with which they are coded has a great impact on the compression ratio In fact up to 40% of the bits transmitted by a codec might be taken up with motion vector data fortunately, the high correlation between motion vectors and their non-uniform distribution makes them suitable for further compression. This compression must be lossless. Efficient coding of motion vectors is a subject of research in its own right and many authors have offered suggestions on which techniques work best. Any one of the lossless general purpose compression algorithms is suitable for coding vectors

Prediction Error coding

Although the battery of techniques described thus far can code video very successfully, they rarely generate perfect replicas of the original frames. Thus the difference between a predicted frame and the original uncompressed frame might be coded. Generally this is applied on a block by block basis and only where portions of the coded frame are significantly different from the original. Transform coding is most frequently used to achieve this and completely lossless coding is rarely a goal.

VIDEO CODEC

A video codec is a device is software that enables compression or decompression of digital video. The compression is usually lossy. Historically, video was stored as an analog signal on magnetic tape. Around the time when the compact disc entered the market as a digital-format replacement for analog audio, it became feasible to also begin storing and using video in digital form and a variety of such technologies began to emerge. a high level picture of a video encoder, applicable to MPEG-2, MPEG-4 or H.264 standards. As digital “raw” video arrives at the encoding pipeline, a series of transforms are applied with the purpose of removing redundant information (lossless) or irrelevant information (lossy) exploiting characteristics of the human visual system (HVS). One important aspect of a video encoder is the removal of temporal redundancy. This is achieved by the Motion Estimation (ME) block in the encoder.
The objective is to represent each video frame as a difference in pixel values from a reference frame. The reference frame can be one or more past frames in the video sequence or frames in the future. Frames, or frame portions, not predicted from references are nominated as INTRA, frames, or frame portions, predicted from a reference are known as INTER. The selection of whether to encode as INTRA or INTER is based on the temporal predictability of the frame or frame portion with respect to the reference.
Typical motion estimation algorithms account for a substantial portion of the application CPU cycles, and in many cases (depending on the complexity of the algorithms) it’s been known to account for as much as 60% of total CPU cycles.
The idea is to achieve bit rate reduction without sacrificing the quality of video signal.
Therefore, this paper targets the analysis of the Motion Estimation algorithms in typical processor micro-architectures with on-chip memory, The proposed algorithm uses the same type of patterns used in diamond search algorithm with a reduction in a step size. Based on the location of the minimum block distortion point (MBD) point, the number of checking points to be used in the successive steps varies. The search pattern shows in the figures 5.
The number of searching steps is reduced to three and the Small Diamond search pattern (SDSP) search is reached at the third step regardless of the location of the MBD point. The Large Diamond Search Pattern (LDSP) is repeatedly used until the centre point becomes the MBD point. The algorithm is summarized as follows. The number of searching steps is reduced to three and the SDSP search is reached at the third step regardless of the location of the MBD point Thus the compact configuration and reduced number of search points provides an improved performance than the other existing algorithms. the centre point becomes the MBD point. Thus the compact configuration and reduced number of search points provide an improved performance than the other existing algorithms given below.

Algorithm

Step 1: Initial LDSP is centered at the origin of the search window. Now, test each point in the Search pattern. If the MBD point is the center point go to step3. Otherwise go to step2.
Step 2: Form a new LDSP with the MBD point as the center point. If the new MBD point is at the center position, go to step3. Otherwise repeat this step for one more time.
Step 3: Form the SDSP with previous MBD point as the center point. The new MBD point obtained in this step becomes the final solution i.e., the motion vector (x, y). The number of search points depends on the location of MBD point also determines the search direction.

STEREO VISION SYSTEM

Two cameras are used, mounted with different perspectives of an object, and calibration techniques are used to align pixel information between the cameras and extract depth information. This is most similar to how our brains work to visually measure distance. The diagram of a simplified stereo vision setup, where both cameras are mounted perfectly parallel to each other, and have the exact same focal length.
The variables in Figure 6 are:
b is the baseline, or distance between the two cameras
f is the focal length of a camera
XA is the X-axis of a camera
ZA is the optical axis of a camera
P is a real-world point defined by the coordinates X, Y, and Z
uL is the projection of the real-world point P in an image acquired by the left camera
uR is the projection of the real-world point P in an image acquired by the right camera
Since the two cameras are separated by distance “b”, both cameras view the same real-world point P in a different location on the 2-dimensional images acquired. The X-coordinates of point’s uL and uR are given by:
uL = f * X/Z and uR = f * (X-b)/Z
Distance between those two projected points is known as “disparity” and we can use the disparity value to calculate depth information, which is the distance between real-world point “P” and the stereo vision system.
Disparity = uL – uR = f * b/z
Depth = f * b/disparity
Stereo vision systems are best suited for applications in which the cameras settings and locations are fixed, and won’t experience large disturbances. Common applications include navigation, industrial robotics, automated inspection and surveillance.

STEREO VISION SYSTEM APPLICATIONS

Stereo vision systems are best suited for applications in which the cameras settings and locations are fixed, and won’t experience large disturbances. Common applications include navigation, industrial robotics, automated inspection and surveillance.

Navigation

Autonomous vehicles use depth information to measure the size and distance of obstacles for accurate path planning and obstacle avoidance. Stereo vision systems can provide a rich set of 3D information for navigation applications, and can perform well even in changing light conditions.

Industrial Robotics

A stereo vision system is useful in robotic industrial automation of tasks such as bin picking or crate handling. A binpicking application requires a robot arm to pick a specific object from a container that holds several different kinds of parts. A stereo vision system can provide an inexpensive way to obtain 3D information and determine which parts are free to be grasped. It can also provide precise locations for individual products in a crate and enable applications in which a robot arm removes objects from a pallet and moves them to another pallet or process.

Automated Inspection

3D information is also very useful for ensuring high quality in automated inspection applications. You can use stereo vision to detect defects that are very difficult to identify with only 2-dimensional images. Ensuring the presence of pills in a blister pack, inspecting the shape of bottles and looking for bent pins on a connector are all examples of automated inspection where depth information has a high impact on ensuring quality.

Surveillance

Stereo vision systems are also good for tracking applications because they are robust in the presence of lighting variations and shadows. A stereo vision system can accurately provide 3D information for tracked objects which can be used to detect abnormal events, such as trespassing individuals or abandoned baggage. Stereo vision systems can also be used to enhance the accuracy of identification systems like facial recognition or other biometrics.

CONCLUSION

The internet is more and more universal and the technology of multimedia and stereo vision systems has been progressed, the communication of the image data is a part in life. In order to employ effect in a limit transmission bandwidth, to convey the most, high quality user information. It is necessary to have more advanced compression method in image and data. In video compression motion estimation is the most computational part and it takes the 70% to 90% complexity Motion estimation and compensation techniques, which can eliminate temporal redundancy between adjacent frames effectively, have been widely applied to popular video compression coding standards such as MPEG-2, MPEG-4 and H.264. Full search Motion Estimation algorithm is not fir for real time applications because of its unacceptable computational cost. Bidirectional ME forms a major noise in video sequences, prediction of missing data in video sequence, so the idea is a Fast Motion Estimation algorithm to improve the operation.
In this paper, to speed up the search, a three step diamond search algorithm based different evolutionary computing techniques is proposed. The proposed bidirectional algorithm is giving less prediction error and the number of search points per each frame is less. The proposed algorithm simulation results shows minimum error and less number of search points when compared to the existing algorithms. These searching algorithms are limited by search speed and pattern so that very quick moments it can’t find exact motion vector. So we can try different search patterns and In future other evolutionary computing techniques also can be tried for the better results. Three important factors Block size, search area, matching criteria can be varied such as Variable block size, large search area for complex motions and small search area for low complex motions and bidirectional motion estimation also we will try to implement new techniques which will further reduce the complexity of MPEG video coding. This algorithm based motion tracking system gives better video quality and gives accuracy in predicting movements so, this can be effectively employed in stereovision systems.

Figures at a glance

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References