Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.Comment: Accepted for National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2019

Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and J. M. Martínez, "Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques" in Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. AVSS 2009, p. 25 - 30In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS for AVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.This work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad"), the Spanish Administration agency CDTI (CENIT-VISION 2007-1007), by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S-050/TIC-0223- ProMultiDis), by the Consejería de Educación of the Comunidad de Madrid, and by The European Social Fund

Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm.

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective

Robust unattended and stolen object detection by fusing simple algorithms

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. San Miguel, and J. M. Martínez, "Robust unattended and stolen object detection by fusing simple algorithms", in IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 2008. AVSS '08, 2008, p. 18 - 25In this paper a new approach for detecting unattended or stolen objects in surveillance video is proposed. It is based on the fusion of evidence provided by three simple detectors. As a first step, the moving regions in the scene are detected and tracked. Then, these regions are classified as static or dynamic objects and human or nonhuman objects. Finally, objects detected as static and nonhuman are analyzed with each detector. Data from these detectors are fused together to select the best detection hypotheses. Experimental results show that the fusion-based approach increases the detection reliability as compared to the detectors and performs considerably well across a variety of multiple scenarios operating at realtime.This work is supported by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S- 050/TIC-0223 - ProMultiDis-CM), by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund

Detection of Parked Vehicles using Spatio-temporal Maps

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels. © 2011 IEEE.This work was supported by the Spanish Government project Movilidad y automocion en Redes de Transporte Avanzadas (MARTA) under the Consorcios Estrategicos Nacionales de Investigacion Tecnologica (CENIT) program and the Comision Interministerial Ciencia Y Tecnologia (CICYT) under Contract TEC2009-09146. The Associate Editor for this paper was R. W. Goudy.Albiol Colomer, AJ.; Sanchis Pastor, L.; Albiol Colomer, A.; Mossi García, JM. (2011). Detection of Parked Vehicles using Spatio-temporal Maps. IEEE Transactions on Intelligent Transportation Systems. 12(4):1277-1291. https://doi.org/10.1109/TITS.2011.2156791S1277129112

Analysis of Illegal Parking Behavior in Lisbon: Predicting and Analyzing Illegal Parking Incidents in Lisbon´s Top 10 Critical Streets

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIllegal parking represents a costly and pervasive problem for most cities, as it not only leads to an increase in traffic congestion and the emission of air pollutants but also compromises pedestrian, biking, and driving safety. Moreover, it obstructs the flow of emergency vehicles, delivery services, and other essential functions, posing a significant risk to public safety and impeding the efficient operation of urban services. These detrimental effects ultimately diminish the cleanliness, security, and overall attractiveness of cities, impacting the well-being of both residents and visitors alike. Traditionally, decision-support systems utilized for addressing illegal parking have heavily relied on costly camera systems and complex video-processing algorithms to detect and monitor infractions in real time. However, the implementation of such systems is often challenging and expensive, particularly considering the diverse and dynamic road environment conditions. Alternatively, research studies focusing on spatiotemporal features for predicting parking infractions present a more efficient and cost-effective approach. This project focuses on the development of a machine learning model to accurately predict illegal parking incidents in the ten highly critical streets of Lisbon Municipality, taking into account the hour period and whether it is a weekend or holiday. A comprehensive evaluation of various machine learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm emerged as the top performing model. The KNN model exhibited robust predictive capabilities, effectively estimating the occurrence of illegal parking in the most critical streets, and together with the creation of an interactive and user-friendly dashboard, this project contributes valuable insights for urban planners, policymakers, and law enforcement agencies, empowering them to enhance public safety and security through informed decision-making

Scene segmentation using similarity, motion and depth based cues

Segmentation of complex scenes to aid surveillance is still considered an open research problem. In this thesis a computational model (CM) has been developed to classify a scene into foreground, moving-shadow and background regions. It has been demonstrated how the CM, with the optional use of a channel ratio test, can be applied to demarcate foreground shadow regions in indoor scenes illuminated by a fixed incandescent source of light. A combined approach, involving the CM working in tandem with a traditional motion cue based segmentation method, has also been constructed. In the combined approach, the CM is applied to segregate the foreground shaded regions in a current frame based on a binary mask generated using a standard background subtraction process (BSP). Various popular outlier detection strategies have been investigated to assess their suitabilities in generating a threshold automatically, required to develop a binary mask from a difference frame, the outcome of the BSP. To evaluate the full scope of the pixel labeling capabilities of the CM and to estimate the associated time constraints, the model is deployed for foreground scene segmentation in recorded real-life video streams. The observations made validate the satisfactory performance of the model in most cases. In the second part of the thesis depth based cues have been exploited to perform the task of foreground scene segmentation. An active structured light based depthestimating arrangement has been modeled in the thesis; the choice of modeling an active system over a passive stereovision one has been made to alleviate some of the difficulties associated with the classical correspondence problem. The model developed not only facilitates use of the set-up but also makes possible a method to increase the working volume of the system without explicitly encoding the projected structured pattern. Finally, it is explained how scene segmentation can be accomplished based solely on the structured pattern disparity information, without generating explicit depthmaps. To de-noise the difference frames, generated using the developed method, two median filtering schemes have been implemented. The working of one of the schemes is advocated for practical use and is described in terms of discrete morphological operators, thus facilitating hardware realisation of the method to speed-up the de-noising process

Recherche par le contenu adaptée à la surveillance vidéo

Les systèmes de surveillance vidéo sont omniprésents dans les lieux publics achalandés et leur présence dans les lieux privés s'accroît sans cesse. Si un aéroport ou une gare de trains peut se permettre d'employer une équipe de surveillance pour surveiller des flux vidéo en temps réel, il est improbable qu'un particulier effectue une telle dépense pour un système de surveillance à domicile. Qui plus est, l'utilisation de vidéos de surveillance pour l'analyse criminalistique requiert souvent une analyse a posteriori des événements observés. L'historique d'enregistrement correspond souvent à plusieurs jours, voire des semaines de vidéo. Si le moment où s'est produit un événement d'intérêt est inconnu, un outil de recherche vidéo est essentiel. Un tel outil a pour objectif d'identifier les segments de vidéo dont le contenu correspond à une description approximative de l'événement (ou de l'objet) recherché. Ce mémoire présente une structure de données pour l'indexation du contenu de longues vidéos de surveillance, ainsi qu'un algorithme de recherche par le contenu basé sur cette structure. À partir de la description d'un objet basée sur des attributs tels sa taille, sa couleur et la direction de son mouvement, le système identifie en temps réel les segments de vidéo contenant des objets correspondant à cette description. Nous avons démontré empiriquement que notre système fonctionne dans plusieurs cas d'utilisation tels le comptage d'objets en mouvement, la reconnaissance de trajectoires, la détection d'objets abandonnés et la détection de véhicules stationnés. Ce mémoire comporte également une section sur l'attestation de qualité d'images. La méthode présentée permet de déterminer qualitativement le type et la quantité de distortion appliquée à l'image par un système d'acquisition. Cette technique peut être utilisée pour estimer les paramètres du système d'acquisition afin de corriger les images, ou encore pour aider au développement de nouveaux systèmes d'acquisition