Disparity with respect to a local reference plane as a dominant cue for stereoscopic depth relief

Earlier studies showed that the disparity with respect to other visible points could not explain stereoacuity performance, nor could various spatial derivatives of disparity [Glennerster, A., McKee, S. P., & Birch, M. D. (2002). Evidence of surface-based processing of binocular disparity. Current Biology, 12:825-828; Petrov, Y., & Glennerster, A. (2004). The role of the local reference in stereoscopic detection of depth relief. Vision Research, 44:367-376.] Two possible cues remain: (i) local changes in disparity gradient or (ii) disparity with respect to an interpolated line drawn through the reference points. Here, we aimed to distinguish between these two cues. Subjects judged.. in a two AFC paradigm, whether a target dot was in front of a plane defined by three reference dots or, in other experiments, in front of a line defined by two reference dots. We tested different slants of the reference line or plane and different locations of the target relative to the reference points. For slanted reference lines or plane, stereoacuity changed little as the target position was varied. For judgments relative to a frontoparallel reference line, stereoacuity did vary with target position, but less than would be predicted by disparity gradient change. This provides evidence that disparity with respect to the reference plane is an important cue. We discuss the potential advantages of this measure in generating a representation of surface relief that is invariant to viewpoint transformations. (c) 2006 Elsevier Ltd. All rights reserved.

Evaluation of probabilistic occupancy map people detection for surveillance systems

In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.