A video-based monitoring system must be capable of continuous operation under various weather and illumination conditions. Moreover, background subtraction is a very important part of surveillance applications for successful segmentation of objects from video sequences, and the accuracy, computational complexity, and memory requirements of the initial background extraction are crucial in any background subtraction method. This paper proposes an algorithm to extract initial color backgrounds from surveillance videos using a probability-based background extraction algorithm. With the proposed algorithm, the initial background can be extracted accurately and quickly, while using relatively little memory. The intrusive objects can then be segmented quickly and correctly by a robust object segmentation algorithm. The segmentation algorithm analyzes the threshold values of the background subtraction from the prior frame to obtain good quality while minimizing execution time and maximizing detection accuracy. The color background images can be extracted efficiently and quickly from color image sequences and updated in real time to overcome any variation in illumination conditions. Experimental results for various environmental sequences and a quantitative evaluation are provided to demonstrate the robustness, accuracy, effectiveness, and memory economy of the proposed algorithm.

A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm

This paper proposes a new background subtraction algorithm based on the sigma-delta filter, which is intended to be used in urban traffic scenes. The original sigma-delta algorithm is a very interesting alternative due to its high computational efficiency. However, the background model quickly degrades in complex urban scenes because it is easily “contaminated” by slow-moving or temporarily stopped vehicles. Then, subsequent foreground validation steps are needed to refine the foreground detection mask. Instead of requiring any subsequent processing steps or resorting to algorithms with higher computational cost, the proposed algorithm tries to achieve a more stable background model by introducing a confidence measurement for each pixel. This confidence measurement assists in a selective background-model updating mechanism at the pixel level. Experimental comparative tests and a quantitative performance evaluation over typical urban traffic sequences corroborate the benefits of the proposed algorithm.

An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Scenes

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.

Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is defined. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

/pdf/video-analysis-based-vehicle-detection-and-tracking-using-an-2xa0j8df9b.pdf

Video analysis-based vehicle detection and tracking using an MCMC sampling framework

This article presents a visual application which allows a study and analysis of traffic behavior on major roads (more specifically freeways and highways), using as the main surveillance artefact a video camera mounted on a relatively high place (such as a bridge) with a significant image analysis field. The system described presents something new which is the combination of both traditional traffic monitoring systems, that is, monitoring to get information on different traffic parameters and monitoring to detect accidents automatically. Therefore, we present a system in charge of compiling information on different traffic parameters. It also has a surveillance module for that traffic, which can detect a wide range of the most significant incidents on a freeway or highway.

J.-M. Lee

Papers

Neural-edge-based vehicle detection and traffic parameter extraction