This paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackers' motion...
Poor visibility conditions due to murky water, bad weather, dust and smoke severely impede the performance of vision systems. Passive methods have been used to restore scene contr...
Mohit Gupta, Srinivasa G. Narasimhan, Yoav Y. Sche...
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
We propose an algorithm for accurate tracking of (articulated) objects using online update of appearance and shape. The challenge here is to model foreground appearance with histo...
Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does n...
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common i...
Robustness to illumination variations is a key requirement for the problem of change detection which in turn is a fundamental building block for many visual surveillance applicati...
This paper presents a novel approach for tracking humans and objects under severe occlusion. We introduce a new paradigm for multiple hypotheses tracking, observe-and-explain, as ...
Estimating the color of a scene illuminant often plays a central role in computational color constancy. While this problem has received significant attention, the methods that exi...
Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered o...