Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> This paper investigates the combination of spatial and probabilistic models for reasoning about pedestrian behaviour in visual surveillance systems. Models are learnt by a multi-step unsupervised method and they are used for trajectory labelling and atypical behaviour detection. <s> BIB001 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> Abstract : The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots. We introduce an approach for estimating the walking direction of people from images, based on learning the correct classification of a still image by using SVMs. We find that the performance of the system can be improved by classifying each image of a walking sequence and combining the outputs of the classifier. Experiments were performed to evaluate our system and estimate the trade-off between number of images in walking sequences and performance. <s> BIB002 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> We present a system for pedestrian detection and impact prediction, from a frontal camera situated on a moving vehicle. The system combines together the output of several algorithms to form a reliable detection and positioning of pedestrians. One of the important contributions of this paper is a highly-efficient algorithm for classification of pedestrian images using a learned set of features, each feature based on a 5/spl times/5 pixels shape. The learning of the features is done using AdaBoost and genetic-like algorithms. The described application was developed as a part of the CAMELLIA project, thus all the algorithms used in this application are designed to use a special set of low level image processing operations provided by the smart imaging core developed in the project. Fusion of the various algorithms results and tracking of pedestrians is done using particle filtering, providing a good tool to predict the future movement of pedestrians, in order to estimate impact probability. <s> BIB003 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> Different approaches to the moving object detection in multi-object tracking systems use dynamic-based models. In this paper we propose the use of a discrete choice model (DCM) of pedestrian behavior and its application to the problem of the target detection in the particular case of pedestrian tracking. We analyze real scenarios assuming to have a calibrated monocular camera, allowing a unique correspondence between the image plan and the top view reconstruction of the scene. In our approach we first initialize a large number of hypothetical moving points on the top view plan and we track their corresponding projections on the image plan by means of a simple correlation method. The resulting displacement vectors are then re-projected on the top view and pre-filtered using distance and angular thresholds. The pre-filtered trajectories are the inputs for the discrete choice behavioral filter used to decide whether the pre-filtered targets are real pedestrians or not. <s> BIB004 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> In this paper a statistical model of pedestrian behavior is proposed. This model is intended to be an important part of a study on the feasibility of car-to-pedestrian accident prediction. The proposed approach is phenomenological as it is based on a four discrete states Markov chain. These four states: "standing still", "walking", "jogging", and "running" are related to the pedestrian pace. First, given the former pedestrian state the current one is calculated. Then, the pedestrian speed vector is split up into the norm and the angle. Information on the statistical distribution of these quantities is available. Their values follow from the present pedestrian discrete state. The proposed model has been compared with related work. It has been used to generate statistically significant pedestrian trajectories and to predict car-to-pedestrian impacts. Simulation results are given based on an evaluation database of car and pedestrian accidents. <s> BIB005 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> A computer simulation of pedestrian flow is an effective method for examining relationships between pedestrian behavior and pedestrian-usable space within a modeled space. In this paper, a model describing pedestrian behavior is proposed by making use of the concept of mental stress. Unknown parameters of the model are estimated using observed data from real pedestrian behavior. The resultant model can describe differences of pedestrian behavior arising from differing walkers' characteristics. Through the results of simulations of the proposed model, we examine comfort and efficiency of pedestrian space. <s> BIB006 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> Vehicle navigation in dynamic environments is an important challenge, especially when the motion of the objects populating the environment is unknown. Traditional motion planning approaches are too slow to be applied in real-time to this domain, hence, new techniques are needed. Recently, iterative planning has emerged as a promising approach. Nevertheless, existing iterative methods do not provide a way to estimating the future behaviour of moving obstacles and to use the resulting estimates in trajectory computation. This paper presents an iterative planning approach that addresses these two issues. It consists of two complementary methods: 1) A motion prediction method which learns typical behaviours of objects in a given environment. 2) An iterative motion planning technique based on the concept of Velocity Obstacles. <s> BIB007 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions. <s> BIB008 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequences. <s> BIB009 </s> Pedestrian Protection Systems: Issues, Survey, and Challenges <s> V. APPROACHES FOR COLLISION PREDICTION <s> In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: emphunconstrained and emphconstrained. The constrained patterns are further classified into emphattractive interactions and emphrepulsive interactions. The formers are captured by a emphleader-follower model while the latters through a emphcollision avoidance model. The spatial correlation between the alternatives is taken into account defining a cross nested logit model. Quantitative analysis is performed by maximum likelihood estimation on a real dataset of pedestrian trajectories, manually tracked from video sequences. <s> BIB010
For a complete safety system, detection should be followed by prediction of the possibility of collision. The system should relay the information to the driver in efficient and nondistracting manner or to the control system of the vehicle in order to take preventive actions. Table VII shows the current research on collision prediction and pedestrian behavior analysis. Deterministic collision prediction approaches project the current trajectories of objects into future and determine the possibility of collision based on geometric computations. In  , which uses LASER scanners for detecting pedestrians, the concept of the region of no escape (RONE) is introduced based on bounds on velocity and acceleration, where the pedestrian-vehicle collision is inevitable. The point of contact and time to collision are accurately determined within this RONE. A traffic-monitoring system proposed in BIB008 is designed to predict various behaviors, including collision between vehicles and pedestrians. The possibility of collision is determined using the zone of interaction, which is defined as an outer ellipse with the same orientation as the target but with larger size. Events where targets are close and have dangerously high relative velocities trigger a potential collision event. Many of the deterministic approaches assume that the speed and direction of pedestrian as well as the vehicle do not change significantly during that time. Such a model is suitable when a vehicle is traveling at high speed, and the time to collision is too short for velocity changes to have a significant effect. However, in situations where the speeds are small, such as at the intersections and pedestrian crossings, effects of velocity changes become important. Also, unlike vehicles, pedestrians are capable of making sudden maneuvers in terms of the speed and direction of motion. Hence, a stochastic model of the pedestrian dynamics is most appropriate for predicting the collision probability. Monte Carlo simulations can then be used to generate a number of possible trajectories based on the dynamic model. The collision probability is then predicted based on the fraction of trajectories that eventually collide with the vehicle. Particle filtering is a natural framework for simultaneously tracking the object and predicting the collision probability BIB003 . In BIB004 , the "discrete choice model" is used in which a pedestrian makes a choice at every step about the speed and direction of the next step. It is assumed that a pedestrian would normally move toward the destination direction, avoid frequent direction changes, and try to adjust speed to a desired speed. In BIB010 , the model is expanded to incorporate the interaction between pedestrians. In BIB009 , the discrete choice behavioral model is integrated with person detection and tracking from static cameras based on image processing in order to improve performance. This approach differs from the conventional tracking since it uses behavior rather than appearance for detection. Also, instead of making hard decisions about target presence on every frame, it integrates the evidence from a number of frames before making a decision. In BIB005 , the pedestrian dynamics is modeled using a hidden Markov model (HMM) with four states corresponding to standing still, walking, jogging, and running. For each state, the probability distributions of absolute speed as well as the change of direction are modeled as truncated Gaussians. In BIB006 , a model of pedestrian behavior in crowds is developed based on "stress" that the pedestrians experience while walking in crowd, including pedestrian stress (P-stress) from other pedestrians, which would push them away, and destination stress (D-stress), which pulls them toward their destination. Other cues, such as orientation of the pedestrian body, can give useful information about the future direction of motion. Hence, estimating the pedestrian orientation can potentially improve the motion prediction and give better estimates of collision probability. In BIB002 , the pedestrian orientation is estimated using the SVM on Haar wavelet coefficients in order to classify between different orientations. In the case of fixed cameras mounted in infrastructure, one can also use the property that pedestrians often follow particular paths. Tracking a large number of pedestrians in the scene can help to learn these paths. For example, in BIB001 a model of probabilistic distribution of trajectories in a scene is generated using a Bayesian HMM-based approach. In BIB007 , a long-term estimate of object motion is obtained using a cluster-based technique to learn motion patterns. Cluster mean value is used to predict motion of new partially observed trajectories. This research could be applied for predicting where a currently detected pedestrian is likely to go and estimate the probability of collision with vehicles. Thus, it is seen that there are various models that are developed for pedestrian behavior analysis. Some of these models have been applied for collision prediction. Monte Carlo simulations to predict future behavior based on pedestrian dynamic models seem to be the most promising approach in obtaining the probability of collision. This approach also fits the particle filtering framework that is widely used for tracking in computer vision systems. For developing a robust system for pedestrian protection, a thorough evaluation of these models, the conditions under which they work, and their performance in real world is required.