Computing the motions of several moving objects in image sequences involves simultaneous motion analysis and segmentation. This task can become complicated when image motion changes significantly between frames, as with camera vibrations. Such vibrations make tracking in longer sequences harder, as temporal motion constancy cannot be assumed. The problem becomes even more difficult in the case of transparent motions.

http://www.wisdom.weizmann.ac.il/~irani/PAPERS/multiple_motions.pdf

Computing occluding and transparent motions

http://www.lfb.rwth-aachen.de/bibtexupload/pdf/AAC93a.pdf

Statistical model-based change detection in moving video

The goal of this paper is to offer a structured synopsis of the problems in image motion computation and analysis, and of the methods proposed, exposing the underlying models and supporting assumptions. A sufficient number of pointers to the literature will be given, concentrating mostly on recent contributions. Emphasis will be on the detection, measurement and segmentation of image motion. Tracking, and deformable motion issues will be also addressed. Finally, a number of related questions which could require more investigations will be presented.

Computation and analysis of image motion: a synopsis of current problems and methods

Motion detection can play an important role in many vision tasks. Yet image motion can arise from "uninteresting" events as well as interesting ones. In this paper, salient motion is defined as motion that is likely to result from a typical surveillance target (e.g., a person or vehicle traveling with a sense of direction through a scene) as opposed to other distracting motions (e.g., the scintillation of specularities on water, the oscillation of vegetation in the wind). We propose an algorithm for detecting this salient motion that is based on intermediate-stage vision integration of optical flow. Empirical results are presented that illustrate the applicability of the proposed methods to real-world video. Unlike many motion detection schemes, no knowledge about expected object size or shape is necessary for rejecting the distracting motion.

/pdf/detecting-salient-motion-by-accumulating-directionally-4li9siwpqe.pdf

Detecting salient motion by accumulating directionally-consistent flow

In many conventional methods for change detection, the detections are carried out by comparing a test statistic, which is computed locally for each location on the image grid, with a global threshold. These ‘nonadaptive’ methods for change detection suffer from the dilemma of either causing many false alarms or missing considerable parts of non-stationary areas. This contribution presents a way out of this dilemma by viewing change detection as an inverse, ill-posed problem. As such, the problem can be solved using prior knowledge about typical properties of change masks. This reasoning leads to a Bayesian formulation of change detection, where the prior knowledge is brought to bear by appropriately specified a priori probabilities. Based on this approach, a new, adaptive algorithm for change detection is derived where the decision thresholds vary depending on context, thus improving detection performance substantially. The algorithm requires only a single raster scan per picture and increases the computional load only slightly in comparison to non-adaptive techniques.

/pdf/bayesian-algorithms-for-adaptive-change-detection-in-image-3fvafwa0mn.pdf

Bayesian algorithms for adaptive change detection in image sequences using Markov random fields

Describes a family of adaptive change detectors for detecting and segmenting moving objects in video scenes. These change detectors employ several techniques to allow them to adjust to changing image conditions. One of these is a dynamic reference frame which allows the system to adapt to changing backgrounds and produces an unambiguous target silhouette for classification. Another is a scheme to estimate system noise and adjust detection thresholds accordingly to satisfy a chosen performance criterion. Finally, adaptive normalization is used to maintain constant image contrast. >

G.W. Donohoe

Papers

Change detection for target detection and classification in video sequences