Lab Meeting 10/28, 2009(Kuen-Han): Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles(PAMI 2008)

Title: Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles(PAMI 2008)
Authors: B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool.

Abstract

Abstract—We present a novel approach for multi-object tracking
which considers object detection and spacetime trajectory
estimation as a coupled optimization problem. Our approach
is formulated in a Minimum Description Length hypothesis
selection framework, which allows our system to recover from
mismatches and temporarily lost tracks. Building upon a stateof-
the-art object detector, it performs multi-view/multi-category
object recognition to detect cars and pedestrians in the input
images. The 2D object detections are checked for their consistency
with (automatically estimated) scene geometry and are converted
to 3D observations, which are accumulated in a world coordinate
frame. A subsequent trajectory estimation module analyzes the
resulting 3D observations to find physically plausible spacetime
trajectories. Tracking is achieved by performing model selection
after every frame. At each time instant, our approach searches for
the globally optimal set of spacetime trajectories which provides
the best explanation for the current image and for all evidence
collected so far, while satisfying the constraints that no two
objects may occupy the same physical space, nor explain the
same image pixels at any point in time. Successful trajectory
hypotheses are then fed back to guide object detection in future
frames. The optimization procedure is kept efficient through
incremental computation and conservative hypothesis pruning.
We evaluate our approach on several challenging video sequences
and demonstrate its performance on both a surveillance-type
scenario and a scenario where the input videos are taken from
inside a moving vehicle passing through crowded city areas.

link

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