Occupancy grid mapping

Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known. Occupancy grids were first proposed by H. Moravec and A. Elfes in 1985. The basic idea of the occupancy grid is to represent a map of the environment as an evenly spaced field of binary random variables each representing the presence of an obstacle at that location in the environment. Occupancy grid algorithms compute approximate posterior estimates for these random variables. Algorithm outline There are four major components of occupancy grid mapping approach. They are:

Interpretation
Integration
Position estimation
Exploration

Occupancy grid mapping algorithm The goal of an occupancy mapping algorithm is to estimate the posterior probability over maps given the data: p(m\mid z_{1:t}, x_{1:t}),

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Real time multi-object tracking using multiple cameras

Michail Zervos

We build a real-time multi-people tracker, which is based on the Kalman Filter. The input to the software is a Probabilistic Occupancy Map of the observed area. The main goal of the project is to incorporate this tracker to the real-time detection software available on the CVLab demo room. A standalone version is also built. The algorithm exploits appearance cues to prevent identity switches. Instead of computing the appearance difference in a frame-by-frame manner, an appearance model is initially built when an individual enters the scene and is afterwards matched against the detected people. The frame-by-frame spatial tracking of the Kalman Filter makes the algorithm computationally efficient and the appearance model matching increases the robustness. The experiments performed in the demo room show that the method is satisfactory. We also validate our algorithm on a few datasets and the results prove that the method can be used in many scenarios. In certain datasets it even outperforms the state-of-the-art method while it’s one to two orders of magnitude faster.

2012

Fixed Point Probability Field for Occlusion Handling

François Fleuret, Pascal Fua

In this paper, we show that in a multi-camera context, we can effectively handle occlusions at each time frame independently, even when the only available data comes from the binary output of a fairly primitive motion detector. We start from occupancy probability estimates in a top view and rely on a generative model to yield probability images to be compared with the actual input images. We then refine the estimates so that the probability images match the binary input images as well as possible. We demonstrate the quality of our results on several sequences involving complex occlusions.

2004

Multi-Camera People Tracking with a Probabilistic Occupancy Map

Jérôme Berclaz, François Fleuret, Pascal Fua

Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each one of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another.

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