Literature Review on Real Time People Tracking in a Camera Network

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International Journal of Electrical, Electronics and Computer Systems (IJEECS)

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ISSN (Online): 2347-2820, Volume -4, Issue-4, 2016 42

Literature Review on Real Time People Tracking in a Camera Network

1Khan Farheen Wahab, ²Aniruddha Kailuke

1,2Department of Electronics (Communication), Priyadarshini Institute of Engineering, The University of Nagpur (RTMNU)Nagpur

Abstract: Image processing is a term which indicates the processing on image or video frame which is taken as an input and the result set of processing is may be a set of related parameters of an image. The used technique is to compare pixel by pixel a still frame from the video (the background image) with all other frames. Every time the pixels of a frame differ from the ones of the background image a simple comparation is done to get the location of those pixels and a red rectangle appears on screen following those pixels Some methods commonly use in it are background subtraction, Frame difference, template matching and shape based methods. We are going to discuss issues about detection and tracking. To analyse and study object detecting and tracking a literature review on some issue related to the subject is done and on the basis some concluded points are stated in the paper

Review On : A SIFT-based Mean Shift Algorithm for Moving Vehicle Tracking

A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences The Authur of the paper “Liang Wei” has tried to explain All the factors result in complex surroundings for object tracking. To some extent, SIFT features are invariant of the above factors. In this paper, a new mean shift tracking algorithm based on SIFT matching is proposed, which combines SIFT and mean shift algorithm. Our method is tested based on actual traffic The Author Proposed in his future proposal the tracking method which is used for moving vehicles with occlusions. In addition, besides the SIFT features, some other features can also be considered to express the vehicle object more accurately and to improve the tracking performance video and the experimental results

show that our method can achieve a good tracking accuracy. Similarly, the proposed method can also be utilized in other traffic scenes, such as pedestrian tracking and multi-view vehicle tracking.

Review On: Tracking in a Camera Network

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects

The Author in the article has tried to explain a new approach to tracking human subjects in video sequences.

First, we have introduced a parametric ellipsoid model in both detection and visual tracking. For detection, this is projected at static grid positions to find intersections between potential subject positions and foreground image data, as determined by mixture of Gaussian segmentation. For tracking, the ellipsoid is parameterized by position, velocity and height as part of the state vectors of a particle filter. As the subject moves, a 3-D appearance description using texture and color is learned progressively. This allows us to integrate observations from multiple cameras into the likelihood function

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Review On : Kernel-Based Structural Binary Pattern Tracking

The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.

The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications:

exploitation of background information, Kalman tracking using motion models, and face tracking.

This paper emphasizes the other type of tracking method target representation and localization. While the filtering and data association have their roots in control theory, algorithms for target representation and localization are specific to images Many tracking algorithms for target representation and localization have been proposed to overcome the difficulties arising from sudden illumination changes, partial occlusion, a similar colored background, and low illumination

In this paper, the author has proposed a new tracking algorithm that can simultaneously overcome the difficulties associated with drastic illumination change, partial occlusion, a similar colored background, and low illumination. For the proposed tracking algorithm, we introduced the binary pattern-based SBP model that consists of a set of multiple SBPs. In addition, we proposed a kernel-based similarity measure between two SBP models for target localization. To further improve the tracking performance, we also employed the CAT method along with the SBP-based tracking method Review On : Object Tracking under Illumination Variations using

2D-Cepstrum Characteristics of the Target

In this paper, a novel object tracking algorithm increasing the robustness of both covariance and codifference tracking methods under varying illumination conditions is proposed. The proposed object tracking algorithm introduces the 2Dcepstrum analysis of the target region to the covariance and codifference tracking

methods. The light intensity-independent 2D-cepstrum coefficients of the target region are used to increase the robustness of the object tracking algorithms to varying illumination conditions

The author briefly explained methods that are tested under abrupt illumination changes, continuously varying light intensity conditions and in the presence of a clutter.

It is clear from our comparisons that there is need to adapt the changes in the target model under varying illumination conditions for robust object detection and accurate object recognition. It is observed that the introduction of the output 2D-cepstrum values of The target region to the covariance and co-difference matrices increases the robustness of the tracking algorithms to light intensity changes.

The Author has concluded with The proposed object tracking method combines the covariance tracking method and the 2D-cepstral features of the target region.

The 2D-cepstrum is used because the cepstrum retains the underlying color and texture information under light- intensity variations. The method is applied to video sequences in which the intensity of the target region varies and it is experimentally observed that the proposed method produces better results than the ordinary covariance tracking method.

Conclusion about the methods in the above research papers

With each simulation result, the following conclusions are drawn regarding the relative performances of kalman filter and mean shift algorithms:-

- The Mean shift algorithm fails to perform when the object is under any kind of motion other than pure translational motion, while the kalman filter is observed to be much more flexible in this regard.

The Kalman filter performs much better than Mean shift algorithm under noisy atmospheric conditions e.g.

rainy ,hazy condition etc. - Even when only translational motion is taken into account, the kalman filter results in much lower RMS error and is much faster initially, as compared to Mean shift algorithm.

On the basis of comparision and detail study of the methods used to detect and track moving object a combined flowchart is implemented The intention of combined method is to concentrate the advantages of the classic methods used in vidio object tracking and apply it into practice. The process of combined method is to analyse the features of objects and chose one or several

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methods according to characters of tracking methods and the demands

Fig.1 The flow chart of combined method DETAILED ANALYSIS OF METHODS Methods

During Our literature review we study the basic method for the Object Detection and tracking, all that methodologies are describe bellow:

1.Background Subtraction Method

A very widely used method which is simple to implement by just subtracting the current frame from previous frame and obtaining threshold value of difference between given pixel value and obtained pixel value. If threshold value is greater than the given the pixel it is considered as foreground. This method is not as appropriate as it is highly inaccurate and gives false rate detection.

2.Real Time Background Subtraction and Shadow 2.1Detection Technique Theory

This method is published in [2] by Mr. Deepjoy Das and Dr. Sarat Saharia, it describes two type of distortion namely brightness distortion and chromaticity distortion based on RGB values of pixels in given image. This method is accurate up to some extends as it also detect the shadow part of object.

2.2. Template Matching

Template Matching is probably the best method for some specific environment. It's the most accurate

although sometimes there is lack of originality in object detected.

3. Difficulties in Object Recognition under Varied Circumstances

All these methods have some feature and limitation in certain circumstances which are defined as follows:

 Lightning: Light differs in many circumstances low light adds darkness in image while more light adds shadow of object.

 Positioning: Template matching needs uniform position or else it is unable to detect object even if it is present in image.

 Rotation: Image can be rotated in any direction.

In this case some shapes are unable to be identified if shape matching method is used.

 Occlusion: Object behind the object is sometimes not completely visible so it cannot be detected and useful part can be ignored

On the basis of the literature review a comparision is done on the methods used for object detection and tracking .The below table shows the method and the factors of comparision

Method Parameter Conclusion

1.

Background Subtraction Method

A very widely used method which is simple to implement

· Objects are allowed to become a part of the background without destroying the existing background.

· It learns itself and does not need to be reprogrammed.

· Can be implemented in any applications.

· Provide fast recovery.

· Low memory requirement

· Highly

inaccurate.

· Cannot deal with

quick changes.

· Initializing the Gaussians is important.

· Not a good subtraction when

shadow, any other

obstacles, are there.

· Gives false positives

· It does not survive with multimodal background.

2. Real Time Background Subtraction and Shadow Detection Technique Theory

· The accuracy of this method is higher than frame difference

· It detects shadow as well.

· The algorithm based on this method is quite complex.

3. Template Matching

· Best method for specific

environment.

· Only occurs when

there‟s a one- toone

match.

· Slow process for

recognize new

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variation of a pattern.

· No scanning process is done on

the percentage so

there is no guaranteed accuracy.

· It only works if

the object is always in the video, otherwise it

will create a false

detects 4. Image

Differencing

· Simple and straight forward.

· Easy to interpret the result.

· Different value is

absolute so value

may have different meaning.

· Require atmospheric calibration.

· Requires selection of thresholds.

5. Shape Based

· Simple pattern matching

approach.

· Having unable to moderate accuracy

· More striking technique.

· Often used as a replacement to local features.

· Does not work well in dynamic situations.

· Unable to determine internal movements well.

· Computational Time is low.

6. Optical Flow

· It can produce the complete

· Object moving information.

· Contain enough accuracy.

· Require large amount of calculation

7. Frame Differencing g

· Perform well for static background.

· High accuracy.

· Easiest method

· It must require a

background without moving objects.

· Method having Computational time low to moderate

REFERENCES:

[1] Robust Object Tracking via Sparse Collaborative Appearance Model Wei Zhong, Huchuan Lu, Senior Member, IEEE, and Ming-Hsuan Yang, Senior Member, IEEE 2014

[2] Mr. Deepjoy Das and Dr. Sarat Saharia,”

Implementation and Performance Evaluation of Background Subtraction Algorithms”, Internationa Journal on Computational Sciences

& Applications (IJCSA) Vol.4, No.2, April 2014 [3] Optimal color-based mean shift algorithm for

tracking objects Xiaowei An, Jaedo Kim, Youngjoon Han Department of Electronic Engineering, Soongsil University, Seoul, Korea Published in IET Computer Vision 2014

[4] Oct. 2008. Y. Li, H. Ai, T. Yamashita, S. Lao, and M. Kawade, “Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans,”

IEEE Trans. Pattern Anal. Mach. Intell., vol.

30,no. 10, pp. 1728–174

[5] Kernel-Based Structural Binary Pattern Tracking Dae-Hwan Kim, Hyo-Kak Kim, Seung-Jun Lee, Won-Jae Park, and Sung-JeaKo, Fellow, IEEE [6] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang,

“Incremental learning for robust visual tracking,” Int. J. Comput. Vis., vol. 77, nos. 1–

3,pp. 125–141, 2008.J. Kwon and K. M. Lee,

“Visual tracking decomposition,” in Proc. IEEE Conf. CVPR, Jun. 2010, pp. 1–10.

[7] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” Int.. Comput.

Vis., vol. 88, no. 2, pp. 303–338, Jun. 2010.

[8] Z. Khan, I. Y. H. Gu, and A. Backhouse, “Robust visual object tracking using multi-mode anisotropic mean shift and particle filters,” IEEE Trans. Circuits Syst. Video Technol., vol. 21, no.

1, pp. 74–87, Jan. 2011.

[9] Fang, J., Yang, J., Liu, H.: „Efficient and robust fragments-based multiple Kernels tracking‟, AEU-Int. J. Electron. Commun., 2011, 65,(11), pp. 915–923

[10] Zhao, Q., Tao, H.: „Object tracking using colourcorrelogram‟. Proc. Joint IEEE Int.

Workshop on VS-PETS, 2005, pp. 263–270 [11] X. Wang, T. Han, and S. Yan, “An HOG-LBP

human detector with partial occlusion handling,”

in Proc. IEEE 12th Int. Conf. Comput. Vis., Oct.

2009, pp. 32–39.

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