AN OPTIMISED FRAME WORK FOR MOVING TARGET DETECTION FOR UAV APPLICATION

Transcription

1 AN OPTIMISED FRAME WORK FOR MOVING TARGET DETECTION FOR UAV APPLICATION Md. Shahid, Pooja HR # Aeronautical Development Establishment(ADE), Defence Research and development Organization(DRDO), Bangalore # Siddaganga Institute of Technology(SIT), Tumkur

2 Agenda ADE: UAV Scenario Introduction Problem Statement Acquisition and Tracking Proposed Frame work Evaluation Criteria Results Analysis Conclusion Scope of future work Discussion

3 ADE: UAV Scenario LAKSHYA Aerial target NISHANT TUAV RUSTOM-1 UAV

4 Introduction Unmanned Aerial Vehicles (UAVs) are increasingly being used for reconnaissance and surveillance. Electronics package DTV Camera FLIR Camera GROUND CONTROL STATION (GCS) Electro Optics (EO) Payload

5 Problem Statement Acquisition: Moving target detection under platform disturbances and delays. Manual Automatic Tracking: Tracking target/vehicle independent of speed/maneuvering. Not limited to number of targets. Target Acquisition Target Tracking

6 Why moving target detection? Acquiring moving targets from airborne platform is difficult task due to associated delays. Video Downlink: 200 msec Commands uplink: 400 msec Object displacement: ~ 100 pixels (in acquisition) Command uplink (400 msec) Video downlink (200 msec) Ground Control Station(GCS) Antenna Vehicle

7 Acquisition Problem

8 Tracking

9 Proposed Frame work for Acquisition Input Video Interest point Detection (Eligibility Criteria) Registration (Scene lock) Background Subtraction Clutter reduction Moving targets detected video * MATLAB 2013a and its tool boxes

10 Interest Point Detection Definition: Local image structure around the interest point is rich in terms of local information contents. Examples : Corner, blob, ridge, edge etc. Interest point Detection (Eligibility Criteria) Corner Detection Techniques: Harris Detector. Moravec Operator. Features From Accelerated Segment Test (FAST). Median Method. Registration (Scene lock) Background Subtraction Clutter reduction

11 Corner detection results (a) (b) (a) (b) (c) Figure : Corners detected by (a) Harris Detector (b) Moravec Operator (c) FAST algorithm (d) Median algorithm (d)

12 Complexity Analysis Complexity in terms of computations and memory (per pixel), is as follows ALGORITHM Multiplication operation Arithmetic operation Division/ Comparision operation Memory requirement Robustness Harris Detector Moravec Operator Fast Algorithm Median Algorithm Excellent Moderate Good - 6 1/26 1 Moderate

13 Eligibility Criteria Restricts the eligible candidates(pixels) to be under process for further corner detection. Considering the pixel as a centre for its 5x5 size block A and its four sub-blocks. Sub-blocks are of 2x2 size each H V D A0 A1 A2 A3 A 3 A 1 A 2 A 0 H 2 V Threshold : D 2 > Mean(A) 2 A3 (2x2) A1 (2x2) A2 (2x2) A0 (2x2) Reduces computational burden significantly.

14 Robustness of eligibility criteria Eligibility criteria is robust under Translation Rotation Scaling Noisy environment Computational saving Algorithm/ Technique Processing time (all pixels) Processing time (eligible pixels) Computational Saving Harris Detector ~ sec ~ sec 62% Moravec Operator ~ sec ~ sec 61% FAST Algorithm ~ sec ~ sec 62%

15 Results of eligibility criteria (a) (b) (c) (d) (e) (f) (g) (h) Figure : (a) Original image of size 256x256. (b) Corners detected by Harris Detector. (c) Rotated image (-10 0 ). (d) Corners detected for rotated image. (e) Image affected by Gaussian noise. (f) Corners detected for Image affected by Gaussian noise. (g) Original image resized to 128x128. (h) Corners detected for resized image.

16 Results of eligibility criteria(uav image) (a) (b) (c) (d) (e) (f) (g) (h) Figure : (a) Original image of size 256x256. (b) Corners detected by Harris Detector. (c) Rotated image (-10 0 ). (d) Corners detected for rotated image. (e) Image affected by Gaussian noise. (f) Corners detected for Image affected by Gaussian noise. (g) Original image resized to 130x130. (h) Corners detected for resized image.

17 Registration Why Registration? Arresting the background against platform movement Required for moving or dynamic platform Assumptions Background forms most part of the scene Background interest points moves slower than foreground Interest point Detection (Eligibility Criteria) Registration (Scene lock) Background Subtraction Multiple target tracking Circularization and correlation matching Restricting to least movement, r 2 = x 2 + y 2 Proportional weightage X = (4*x 1 + 3*x 2 )/7, Y = (4*y 1 + 3*y 2 )/7 Discarding poor target & Best target weigh more Unregistered video clip Clutter reduction Registered video clip

18 Background Subtraction Background subtraction Key aspect of the frame work Type of backgrounds Dynamic backgrounds Gradual illumination changes Sudden illumination changes Moved object Shadows Interest point Detection (Eligibility Criteria) Registration (Scene lock) Background Subtraction Various methods Pixel or region based methods Parametric or nonparametric methods Recursive or non-recursive methods Clutter reduction

19 Background Subtraction Methods Bayesian histogram Morphological filtering Sigma-delta( - ) motion detection Visual Background Extractor (ViBe)

20 Static platform (a) (b) (c) (d) (e) (f) Figure : a) Input video frame of static camera. b) Ground truth. c) Bayesian histogram. d) Morphological Filtering. e) - motion detection. f) ViBe method. Courtesy: video sequence Highway II (available at

21 Moving platform (a) (b) (c) (d) (e) (f) Clutter Figure : (a) Input video frame of UAV. (b) Ground truth. (c) Bayesian histogram. (d) Morphological Filtering. (e) - motion detection. f) ViBe method. (Note: Clutter has not been removed)

22 Clutter reduction algorithm Background 1 Subtraction Background subtracted image Remove the area containing less than 200 pixels Find boundaries Find, A o = Area occupied by object & A B = Filled area of bounding box Interest point Detection (Eligibility Criteria) Registration (Scene lock) Fill Ratio, A = A o / A B ( A > 0.5)? Aspect Ratio A r =Width/Height (3.5 > A r > 1 )? Moving 2 Yes Yes Objects No No Clutter Background Subtraction Clutter reduction

23 Evaluation Criteria Subjective: Visual inspection Objective : Where, Percentage of Correct Classification(PCC) False Positive Rate (FPRate) TP TN PCC TP TN FP FN FPRate True positives(tp): Number of correctly detected foreground pixels False positives(fp): Number of background pixels incorrectly classified as foreground True negatives(tn): Number of correctly classified background pixels False negatives(fn): Number of foreground pixels incorrectly classified as background FP FP TN

24 Results Analysis

25 Conclusion MATLAB helped us all the way to develop this frame work for real time UAV application Extensively utilized following MATLAB Tool boxes Computer vision system tool box Image Processing tool box DSP System Processing tool box Statistics tool box Quick study of various methodologies Not limited to number of moving targets Complexity is independent of target speed Reduced time to develop this framework

26 Scope of future work Immediate: Target merged to clutter Fill ratio criteria (a) (b) Figure : (a) Targets merged with clutter. (b) Target failing to fill ratio criteria. Next: Clutter is more due to rolling Replacement of Morphological operation

27 References O. Barnichand M. Van Droogen broeck, "ViBe: A Universal Background Subtraction Algorithm for Video Sequences", IEEE Transactions on image processing, Vol 20, no.6, June A Robust and Computationally Efficient Motion Detection Algorithm Based on - Background Estimation. A. Manzanera J. C. Richefeu. ENSTA/LEI, 32 Bd VictorF PARIS CEDEX 15, july 6, FASTER and better: A machine learning approach to corner detection in IEEE Trans. Pattern Analysis and Machine Intelligence, Edward Rosten, Reid Porter and Tom Drummond, vol 32, pp , Saad Ali and Mubarak Shah, COCOA - TRACKING IN AERIAL IMAGERY. Computer Vision Lab,School of Computer Science,University of Central Florida, A Background Subtraction Model adapted to Illumination changes. Julio Cezar Silveira Jacques Jr., Claudio Rosito Jung and Soraia Raupp Musse, IEEE Transactions, /06/$20.00 C C. Harris and M. Stephens. A Combined Corner and Edge Detector. Proc. Alvey Vision Conf., Univ. Manchester, pp , H. P. Moravec. Towards Automatic Visual Obstacle Avoidance. Proc. 5th International Joint Conference on Artificial Intelligence, pp. 584, 1977.

28 Acknowledgement Aeronautical Development Establishment (ADE), Defence Research and Development Organization (DRDO), Bangalore, India. Department of Electronics & Communication Engg., Siddaganga Institute of Technology (SIT), Tumkur, India. CoreEL Technologies, Bangalore, India.

29 Discussion

30 FOR MORE DETAILS CONTACT Md. Shahid, Pooja H.R,