Andrea Colombari

Segmentation and tracking of multiple video objects

This paper describes a technique that produces a content-based representation of a video shot composed by a background (still) mosaic and one or more foreground moving objects. Segmentation of moving objects is based on ego-motion compensation and on background modelling using tools from robust statistics. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion. The sequence is represented as a layered graph, and specific techniques are introduced to cope with crossing and occlusion. Examples of MPEG-4 (main profile) encoding are reported.

Patch-Based Background Initialization in Heavily Cluttered Video

In this paper, we propose a patch-based technique for robust background initialization that exploits both spatial and temporal consistency of the static background. The proposed technique is able to cope with heavy clutter, i.e, foreground objects that stand still for a considerable portion of time. First, the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then, a tessellation is grown incrementally by selecting at each step the best continuation of the current background. The method rests on sound principles in all its stages and only few, intelligible parameters are needed. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.

Background Initialization in Cluttered Sequences

In this paper we propose a technique to robustly estimate the background in a cluttered sequence, i.e., a sequence where occluding objects persist in the same position for a considerable portion of time. As pixel-level heuristic are not sufficient in this case, we introduce spatial support. First the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then the background is grown incrementally by selecting at each step the best continuation of the current background, according to the principles of visual grouping. The method rests on sound principles in all its stages, and only few, intelligible parameters are needed. Experiments with real sequences illustrate the approach.

Exemplar-based background model initialization

Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing. In this paper, we propose a novel method for BG initialization and recovery, that merges interesting ideas coming from the video inpainting and the generative modelling subfields. The method takes as input a video sequence, in which several objects move in front of a stationary BG. Then, a statistical representation of the BG is iteratively built, discarding automatically the moving objects. The method is based on the following hypotheses: (i) a portion of the BG, called sure BG, can be identified with high certainty by using only per-pixel reasoning and (ii) the remaining scene BG can be generated utilizing exemplars of the sure BG. The proposed algorithm is able to exploit these hypotheses in a principled and effective way.

Video Objects Segmentation by Robust Background Modeling

This paper deals with the problem of segmenting a video shot into a background (still) mosaic and one or more foreground moving objects. The method is based on ego-motion compensation and background estimation. In order to be able to cope with sequences where occluding objects persist in the same position for a considerable portion of time, the papers concentrates on robust background estimation method. First the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then the background is grown incrementally by selecting at each step the best continuation of the current background, according to the principles of visual grouping. The method rests on sound principles in all its stages, and only few, intelligible parameters are needed. Experiments with real sequences illustrate the approach.

FPGA-based robust ellipse estimation for circular road sign detection

Estimating parametric curves from images using robust fitting algorithms is a well-known and important computer vision task. We present a complete FPGA design and implementation of a fast and robust model fitting algorithm for real-time ellipse detection on video streams. The proposed solution relies on a the RANSAC algorithm, modified for FPGA deployment, in combination with an image-preprocessing pipeline in order to perform the intensive pixel-level analysis, reducing each frame to a simple binary image of edges. The design has been developed in a parallel fashion and with specific architectural solutions so as to allow a fast response without degrading the functional performances. Experimental results on synthetic and real data show that our implementation, synthesized onto a Xilinx Spartan-3A DSP 3400A device, succesfully runs in real-time with a low resource occupation, while maintaining a functionality comparable with the floating-point software version.