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CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road survei...
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... Among the various important applications of automatic behavior analysis systems, automatic road surveillance has received increasing interest in recent years. 2 Walking is a common activity for a large portion of the population, especially among the elderly. But, accidentology studies show that this category of road users may sometimes adopt unsafe behaviors when crossing the street. ...
... Although many challenging visual surveillance tasks have been completed in the above studies, the task of considering activities and interactions with complicated temporal structures is not yet addressed. 2 The confidence degree of vision sensors' observations is not propagated in the different levels of BN and taken into account in the final generated decision. ...
... Video surveillance systems prove their efficiency in road traffic patterns identification and behaviors classification. In this regard, the usage of a combination between the PAM (Pachinko allocation model) with the SVM (support vector machine) achieved better performances than the LDA (latent Dirichlet allocation) approach [15]. An innovative proposal shows that traffic patterns can be retrieved from social media applications that enable user geo-location data sharing. ...
Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.
... The system is able to identify certain traffic violations and record the details of these violations in a local database, which makes it possible to display the spatial and temporal data of traffic violations on a geographic map using the standard Google Earth tool. In article [4], a new approach to modeling traffic behaviors is proposed for videobased road monitoring. In the proposed system, the Pachinko allocation model (PAM) and the backup vector machine (SVM) are combined to display hierarchies and identify traffic behaviors. ...
Since the traffic data has a high volume, high diversity and high speed of production, the traditional systems cannot process them accurately. In this paper, a big data based system was designed and implemented for identifying the offenders’ behaviors. The proposed Traffic Violation Detection System (TVD system) included four main phases which were described using the MAPE methodology. In the monitor phase, unstructured data, such as videos captured by traffic control cameras as well as the images and the descriptions provided by traffic officers, were collected. In the analysis phase, the knowledge base of unsafe driving behaviors was created and classified. In this phase, a standard Work Breakdown Structure for unsafe behaviors was created by the experts in traffic control. In the Plan phase, in order to detect unsafe driving behaviors related to police descriptions for collected images, and to detect unsafe behaviors from video cameras, Behavior-Based Safety process with Map/ Reduce technique and Vector Space Model (VSM) were employed. In the last phase, all types of data, including the structured data and the multimedia/unstructured data, along with the types and details of violations, were stored on the Hadoop Distributed File System. The prototype of the proposed TVD system was successfully implemented for a few common violations. The results showed that by applying big data technologies, the driving violations could be detected more accurately using the combination of the structured and unstructured data. The results indicate that compared to the sequential program, Hadoop only with a single slave-node decreases the processing time of big data by more than 70%. Also, by increasing the number of slave nodes from 1 to 7 in the police descriptions and images of surveillance cameras, the processing time reduces by 60.87% and 70%, respectively. Thus, the TVD performance increases by more than 75% as the number of data nodes boosts. Based on the results, it can be concluded that by identifying unsafe driving behaviors, it is possible to diminish traffic accidents and the damage caused by them at a satisfactory level. Also, the authors decided to compare these studies in terms of qualitative criteria, such as fieldwork and behavioral identification, and quantitative criteria.
... Sci. 2020, 10, 5673 2 of 17 conditions [4], providing driver tips to improve their driving comfort and safety [5], recognizing traffic activity and behavior [6], and detecting risky driving conditions [7]. ...
Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.
... Simulated annealing and iterated conditional modes are also implemented which classifies efficiently GM, WM CSF, scalp-bone, and background [36]. Bricq et al. developed a Hidden Markov Chain (HMC) that considers neighborhood information for the ROI extraction in multimodal brain MR images [37]. Ibrahim et al. introduced Hidden Markov Models (HMMs) for the 3D MRI segmentation that handles complexity in segmentation and artifacts, [38]. ...
... Energy values of CH 2 and 4 are observed to be lower than the threshold value and can therefore be supported with relay assistance. The relay users are assumed to be positioned at [ (35,180), (36,182), (37,184), (38,186), (39,188)]. After estimating the distance between CHs and RUs as well as RUs and FC, it is observed that CH2 can be assisted with RU1 and CH4 can be assisted with RU2. ...
... Traffic Behavior Recognition using the Pachinko Allocation Model [37] presents for road surveillance which combines SVM and pachinko allocation model (PAM) to perform behavior classification. The method uses the Kalman filter to track the object using the Gaussian mixture models (GMMs). ...
... Moreover, the simulation and optimization of traffic flows on the basis of a directed acyclic graph is proposed in the paper [17]. The model determines the correlation between the behavior of road users, with a built-in model of decision support. ...
... Either way, it is necessary to generalize the methods, models and means of graph-analytical modeling of distributed technological systems and objects, set forth in the writings [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In the aspect of the application of these categories in order to research the work and automated design of information management systems, one can distinguish such general limitations and disadvantages: ...
... As noted above, all of the graph-analytic models of distributed systems, presented, in particular, in the papers [15][16][17][18][19][20][21][22], reproduce the static properties of the modeled objects. The object dynamic properties are characterized by separate models, which are integrated with the static component at the interface between the application software and the configuration files. ...
An important area of scientific and applied research inthe field of technical sciences and the application of relevantresults is the modeling of complex control systems and theirtechnological objects. The most common is the variation ofthe distribution of such systems in space (taking into accountboth geographical and conditional coordinate features), in Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 4/4 ( 94 ) 201860particular in transportation, energy, industry, etc. Thus, it is convenient and effective to consider the applica-tion of geometric modeling methods. The latter are based,for the most part, on the use of the apparatus of the theoryof graphs. The analytical interpretation of the graphicmodels constructed in this way is carried out with the use oftopological or parametric-topological matrices. The basis ofthese matrices is the systemic anatomical properties of thegraphs, such as: incidence, adjacency, cyclicity, etc. Development and investigation of methods of graphic-functional modeling of distributed systems.
... La limitation commune de la plupart de ces approches basées sur l'HMM est la nécessité d'une grande quantité de données pour la phase d'apprentissage [217]. Par ailleurs, elles ne sont pas tellement adéquates pour les cas des analyses des comportements complexes qui sont par défaut représentés par plusieurs variables et/ou événements. ...
Le trafic routier est devenu de plus en plus intense. Une telle situation avec le manque de prudence des piétons constituent deux causes majeures de l’augmentation des accidents routiers. En France, 16% des accidents de la route en 2016 impliquent au moins un piéton et chaque année, environ de 800 piétons sont tués dans un accident de la circulation. De plus, la part des plus de 65 ans dans la mortalité piétonne est en hausse de 13% entre 2014 et 2016. Ainsi, par ce projet de thèse nous proposons une approche probabiliste pour inférer le type de comportement (à risque ou sécurisé) des piétons lors de la traversé de la rue. Cette approche se compose de 2 couches principales : Une couche basse, basée sur les techniques de vision par ordinateur, pour la collecte des paramètres des piétons, du trafic et des aménagements urbains et une couche haute, basée sur le Réseau Bayésien (RB), pour l’inférence du type de comportement. Plusieurs contributions et améliorations sont proposées pour la construction d’une telle approche que ce soit au niveau de la couche basse (techniques de détection et de suivi utilisées) ou au niveau de la couche haute (gestion des incertitudes des capteurs de vision et la mise en relation des paramètres hétérogènes et variées).
... Request permissions from permissions@acm.org. '17, January 05-07, 2017, Beppu, Japan Despite the wide utilization of foreground detection in visual surveillance systems [7] for both indoor and outdoor scenarios, the authors aim to discuss some major issues consisted of the accuracy of foreground detection for multimodal backgrounds. Compared with difference frame and optical flow approaches, background subtraction based techniques have some advantages for real-time implementation, however, detection accuracy mostly depends on the scene background that has to be estimated by a background modeling algorithm. ...
This paper improves a remarkable background estimation algorithm, namely Neighbor-based Intensity Correction (NIC) which is used in the background subtraction technique for foreground detection. The algorithm has an efficient intensity correction scheme to update the current background based on calculating the standard deviation of two windows captured from the background and the input frame in which the windows are constructed by a squared-structure binary mask. Although the NIC algorithm achieved the comparative results with existing approaches on the foreground detection accuracy and processing speed, its performance in the multi-modal background including high-speed motion and camera jitter should be improved. In the original algorithm, we recognize that the shape of a binary mask further affects the updating performance besides the window size which was already analyzed. Various shapes are therefore recommended for the multi-modal background adaptation. Moreover, an adaptive threshold identified by referring several previous Otsu thresholds to cope with the high-speed motion challenge is proposed. Experimental results on some standard datasets such CAVIAR 2004, AVSS2007, PETS 2009, and CDNET 2014, demonstrate that the foreground detection accuracy is significantly boosted with 2.6--6.7% of the F-measure metric.
... This model was demonstrated to succeed on the unsupervised mining of behaviors in complex and crowded public scenes. An efficient method developed on Pachinko Allocation Model (PAM) was presented in [10] to model the activity and behavior from with fully flexible correlation. ...
... In this paper, the authors continuously improves the method in [10] by considering more specific features to enhance classification accuracy. Firstly, the feature-book comprises the object location, moving direction, speed, and appearance time length, which is constructed from object trajectory information in the temporal-spatial dimension. ...
... The Pachinko Allocation Model concretely described in our work [10] is continuously applied to model codewords into activities and behaviors. Compared to Latent Dirichlet Allocation (LDA), a topic model, PAM provides more flexibility and greater expressive power than LDA model because it captures not only correlations among the words (as in LDA), but also correlation among topics. ...
Recently CCTV-based behavior recognition have gained considerable attention in the transportation surveillance systems to identify normalities, such as traffic jams, accidents, and dangerous driving. An improved method is presented in this paper for the traffic behavior surveillance system by discovering more highly specific features based on the trajectory information. The multiple sparse feature comprising the object location, moving direction, speed, and appearance time length obtained from the moving object detection and tracking stage is modeled by the Pachinko Allocation Model. This hierarchical probabilistic model captures the correlation among the traffic activities and behaviors through the sparse features as the visual words. In the classification phase, the Support Vector Machine constructed from Decision Tree Architecture is utilized. Compared with existing methods, the proposed method outperforms 3-8% approximately in overall classification accuracy.
