An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles

Modeling Pedestrian Behavior in Video

The purpose of this dissertation is to address the problem of predicting pedestrian movement and behavior in and among crowds. Specifically, we will focus on an agent based approach where pedestrians are treated individually and parameters for an energy model are trained by real world video data. These learned pedestrian models are useful in applications such as tracking, simulation, and artificial intelligence. The applications of this method are explored and experimental results show that our trained pedestrian motion model is beneficial for predicting unseen or lost tracks as well as guiding appearance based tracking algorithms. The method we have developed for training such a pedestrian model operates by optimizing a set of weights governing an aggregate energy function in order to minimize a loss function computed between a model\u27s prediction and annotated ground-truth pedestrian tracks. The formulation of the underlying energy function is such that using tight convex upper bounds, we are able to efficiently approximate the derivative of the loss function with respect to the parameters of the model. Once this is accomplished, the model parameters are updated using straightforward gradient descent techniques in order to achieve an optimal solution. This formulation also lends itself towards the development of a multiple behavior model. The multiple pedestrian behavior styles, informally referred to as stereotypes , are common in real data. In our model we show that it is possible, due to the unique ability to compute the derivative of the loss function, to build a new model which utilizes a soft-minimization of single behavior models. This allows unsupervised training of multiple different behavior models in parallel. This novel extension makes our method unique among other methods in the attempt to accurately describe human pedestrian behavior for the myriad of applications that exist. The ability to describe multiple behaviors shows significant improvements in the task of pedestrian motion prediction

Analysis Of Behaviors In Crowd Videos

In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems. We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of n frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, iii the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces. The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors. To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to iv recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic. The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order. We, therefore, propose a new representation for group behaviors of humans using the interrelation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances

An integrated solution based irregular driving detection

Global Navigation Satellite Systems (GNSS) are used widely in the provision of Intelligent Transport System (ITS) services. Today, metre-level positioning accuracy, which is required for many applications including route guidance, fleet management and traffic control can be fulfilled by GNSS-based systems. Because of this level of success and potential, there is an increasing demand for GNSS to support applications with more stringent positioning requirements. These include safety related applications that require centimetre/decimetre level positioning accuracy, with high integrity, continuity and availability such as lane control, collision avoidance and intelligent speed assistance. Detecting lane level irregular driving behaviour is the basic requirement for lane level ITS applications.Currently, some research has addressed road level irregular driving detection, however very little research has been done in lane level irregular driving detection. The two major issues involved in the lane level irregular driving identification are access to high accuracy positioning and vehicle dynamic parameters, and extraction of erratic driving behaviour from this and the lane related information.This thesis proposes an integrated solution for the detection of lane level irregular driving behaviour. Access to high accuracy positioning is enabled by GPS and its integration with an Inertial Navigation System (INS) using Extended Kalman Filtering (EKF) and Particle Filtering (PF) with precise vehicle motion models and lane centre line information. Four motion models are used in this thesis: Constant Velocity (CV), Constant Acceleration (CA), Constant Turn Rate and Velocity (CTRV) and Constant Turn Rate and Acceleration (CTRA). The CV and CA models are used on straight lanes and the CTRV and CTRA models on curved lanes. Lane centre line information is extracted from defined lane coordinates in the simulation and is surveyed and stored as sets of positioning points from the motorway in the field test. The high accuracy vehicle positioning and dynamic parameters include yaw rate (omega) and lateral displacement (d) in addition to conventional navigation parameters such as position, velocity and acceleration. The detection of irregular driving behaviour is achieved by comparing the sorting rules of a driving classification indicator from the filter estimations with what is extracted from the reference. The detected irregular driving styles are characterized by weaving, swerving, jerky driving and normal driving on straight and curved lanes, based on the Fuzzy Inference System (FIS). The solution proposed in the thesis has been tested by simulation and validated by real field data. The simulation results show that different types of lane level irregular driving behaviour can be correctly identified by the algorithms developed in this thesis. This is confirmed by the application of data from a field test during which the dynamics of an instrumented vehicle supplied by Imperial College London were captured in real time. The results show that the precise positioning algorithms developed can improve the accuracy of GPS positioning and that the FIS based irregular driving detection algorithms can detect the different types of irregular driving. The evaluation of the designed integrated systems in the field test shows that a positioning accuracy of 0.5m (95%) source is required for lane level irregular driving detection, with a correct detection rate of 95% and availability of 94% based on a 1s output rate. This is useful for many safety related applications including lane departure warnings and collision avoidance.Open Acces