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Motion based Correspondence for Distributed 3D tracking of multiple dim objects

Motion based Correspondence for Distributed 3D tracking of multiple dim objects. Ashok Veeraraghavan. Problem Setting. Constraints. R, T ??. R, T ??. R, T ??. Bandwidth < W. Bandwidth < W. R, T ??. Outline. Tracking Algorithm Implemented at each camera node.

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Motion based Correspondence for Distributed 3D tracking of multiple dim objects

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  1. Motion based Correspondence for Distributed 3D tracking of multiple dim objects Ashok Veeraraghavan

  2. Problem Setting

  3. Constraints R, T ?? R, T ?? R, T ?? Bandwidth < W Bandwidth < W R, T ??

  4. Outline Tracking Algorithm • Implemented at each camera node. • Correspondence problem for dim targets. • Motion-Based Correspondence Algorithm • Implemented at central processor • Recovering Camera Position and Orientation • Recovering 3D tracks using triangulation.

  5. Experimental Setup • Objective : • Reconstruct the 3D trajectories of the bees so as to study the response of bees to visual stimuli. • Outdoor Bee Tunnel with the surrounding walls texture systematically varied • Study relationship of flight patterns to visual stimulii. • Two Fixed Cameras. • Free Flying bees are the targets to be tracked. • Typically the bees are about 20-50 meters away from the camera. • Multiple Targets: On average each frame contains about 6-8 bees. • Occupy about 5-10 pixels at closet range: Low SNR • Objective : Reconstruct the 3D trajectories of the bees so as to study the response of bees to visual stimuli.

  6. Tracking Algorithm • Background Subtraction • Background variations are assumed to be much slower than the target. • Dynamic background estimated using a temporal low pass filter for each pixel. • Connected Component Analysis • Morphological processing to connect pixels belonging to same target. • Probabilistic Data Association • Blob Tracking algorithm.

  7. Background Subtraction and Connected Component Analysis

  8. Background Subtraction and Connected Component Analysis

  9. Adaptive Velocity Motion Model r v

  10. Correspondence Problem for Dim Targets • Correspondence across camera Views • Associating the objects found in various views • Especially tricky for multiple dim objects • Dim Targets • Low SNR • Very Small Targets – (order of few pixels ) • Features extraction unreliable • Appearance based correspondence • Appearance varies with view • Unreliable for dim targets

  11. Motion Based Correspondence • Rubin and Richards (1985) • Rao, Yilmaz and Shah (2002)- • Maxima of spatio-temporal curvature as Dynamic Instants Courtesy: [Rao2002]

  12. Dynamic Instants • Detects any start instant, stop instant, non-smooth change in speed, maximal curvature of 3D tracks. Eg., Start Instants Courtesy: [Rao2002]

  13. Detected Dynamic Instants Courtesy: [Rao2002]

  14. Correspondence Across Views

  15. External Calibration • Internal Camera parameters known. • External Orientation of the cameras to be estimated from correspondence data obtained by matching tracks across views. • Simple non-linear optimization implemented (Levenberg-Marquardt). • Distance between cameras (Baseline) approximately known. • Optimization is local. Requires good initial estimate.

  16. 3D flight Paths using Triangulation • Internal camera parameters known. • External camera calibration parameters estimated from point correspondences. • 3D tracks obtained using Triangulation.

  17. 3D Flight Paths

  18. 3D Flight Paths

  19. Future Work • Human Surveillance. • Work with multiple (more than 2 cameras) cameras. • Study the trade-off between bandwidth and efficiency. • Especially can we also add some appearance information to each target so that limited view reconstruction of target is possible?

  20. Thank You

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