motion based correspondence for distributed 3d tracking of multiple dim objects ashok veeraraghavan
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Motion based Correspondence for Distributed 3D tracking of multiple dim objects
Ashok Veeraraghavan
Problem Setting
Constraints
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OutlineTracking 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.
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.
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.
Background Subtraction and Connected Component Analysis
Background Subtraction and Connected Component Analysis
Adaptive Velocity Motion Model
v
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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
Motion Based Correspondence Rubin and Richards (1985)
Rao, Yilmaz and Shah (2002)- Maxima of spatio-temporal curvature as Dynamic Instants
Courtesy: [Rao2002]
Dynamic Instants Detects any start instant, stop instant, non-
smooth change in speed, maximal curvature of 3D tracks. Eg., Start Instants
Courtesy: [Rao2002]
Detected Dynamic Instants
Courtesy: [Rao2002]
Correspondence Across Views
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.
3D flight Paths using Triangulation Internal camera parameters known. External camera calibration parameters
estimated from point correspondences. 3D tracks obtained using Triangulation.
3D Flight Paths
3D Flight Paths
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?
Thank You
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