Background subtraction is a widely used approach for detecting moving objects from static cameras. Many different methods have been proposed over the recent years and both the novice and the expert can be confused about their benefits and limitations. In order to overcome this problem, this paper provides a review of the main methods and an original categorisation based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. Methods reviewed include parametric and non-parametric background density estimates and spatial correlation approaches.

/pdf/background-subtraction-techniques-a-review-13jzs7gesy.pdf

Background subtraction techniques: a review

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

/pdf/vibe-a-universal-background-subtraction-algorithm-for-video-3hbi5ckurx.pdf

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.

/pdf/visual-tracking-an-experimental-survey-50w2t40998.pdf

Visual Tracking: An Experimental Survey

Efficient adaptive density estimation per image pixel for the task of background subtraction

Identifying moving objects from a video sequence is a fundamental and 
critical task in many computer-vision applications. A common approach
is to perform background subtraction, which identifies moving objects
from the portion of a video frame that differs significantly from a 
background model. There are many challenges in developing a good
background subtraction algorithm. First, it must be robust against
changes in illumination. Second, it should avoid detecting
non-stationary background objects such as swinging leaves, rain, snow,
and shadow cast by moving objects. Finally, its internal background
model should react quickly to changes in background such as starting
and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.

/pdf/robust-techniques-for-background-subtraction-in-urban-3p4r8wy4bj.pdf

Robust techniques for background subtraction in urban traffic video

The seminal video surveillance papers on moving object segmentation through adaptive Gaussian mixture models of the background image do not provide adequate information for easy replication of the work. They also do not explicitly base their algorithms on the underlying statistical theory and sometimes even suffer from errors of derivation. This tutorial paper describes a practical implementation of the Stauffer-Grimson algorithm and provides values for all model parameters. It also shows what approximations to the theory were made and how to improve the standard algorithm by redefining those approximations.

http://www.cse.psu.edu/~rtc12/CSE586Spring2010/papers/emBGsubtractAboutSandG.pdf

P. Wayne Power

Papers

Understanding Background Mixture Models for Foreground Segmentation