Reliable anticipation of pedestrian trajectory is imperative for the operation of autonomous vehicles and can significantly enhance the functionality of advanced driver assistance systems. While significant progress has been made in the field of pedestrian detection, forecasting pedestrian trajectories remains a challenging problem due to the unpredictable nature of pedestrians and the huge space of potentially useful features. In this work, we present a deep learning approach for pedestrian trajectory forecasting using a single vehicle-mounted camera. Deep learning models that have revolutionized other areas in computer vision have seen limited application to trajectory forecasting, in part due to the lack of richly annotated training data. We address the lack of training data by introducing a scalable machine annotation scheme that enables our model to be trained using a large dataset without human annotation. In addition, we propose Dynamic Trajectory Predictor (DTP), a model for forecasting pedestrian trajectory up to one second into the future. DTP is trained using both human and machine-annotated data, and anticipates dynamic motion that is not captured by linear models. Experimental evaluation confirms the benefits of the proposed model.
Given the absence of large pedestrian trajectory datasets, previous works have modeled the dynamic motion of pedestrian's using linear dynamic systems (LDS) that combine the assumptions of constant velocity (CV) or constant acceleration (CA) with a filtering algorithm such as the Kalman filter @cite_0 . To model non-linear, dynamic motion, a switching linear dynamic system (SLDS) uses a discrete Markov chain to select between multiple LDS at each timestep based on past observations. However, the SLDS is limited to to pedestrian motion rather than a change in dynamics. To address this issue, existing works @cite_16 @cite_3 focus on additional cues such as pedestrian head pose, motion state, and road scene context or use a non-linear filtering algorithm such as the unscented Kalman filter @cite_11 .
@cite_0 @cite_16 @cite_3 @cite_11
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In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity acceleration turn). These are applied to four typical pedestrian motion types (crossing, stopping, bending in, starting). Position measurements are provided by an external state-of-the-art stereo vision-based pedestrian detector. We investigate the accuracy of position estimation and path prediction, and the benefit of the IMMs vs. the simpler single dynamical models. Special care is given to the proper sensor modeling and parameter optimization. The dataset and evaluation framework are made public to facilitate benchmarking. We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method. Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target’s behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method. In this paper we present a time-efficient estimation framework for camera-based pedestrian tracking from a moving host car using a monocular camera. An image processing system processes the camera output to find the location of objects of interest in each frame. The position and sensor information about the host translation and rotation are passed to a tracking module. The module uses the position of the detected objectpsilas foot point as measurement input and connects them over time to estimate the movement of the objects of interest in order to reduce noise and single frame failures in the detection process. We have developed a new method to estimate the target movement which takes into account the host movement and allows to exploit prior information about the intrinsic and extrinsic camera parameters. The basic idea is to assume that host and target movements can be modelled as 2-dimensional movements on a flat ground-plane. Our developed motion model is based on this assumption and includes host motion as well as the target ego motion. A measurement is modelled as a perspective projection of a point on the ground-plane to the image plane. The motion and the measurement model are combined by an unscented Kalman filter. This filter is relatively new and has not been applied for pedestrian tracking before. Finally, we present a new logical initialization strategy for the selected filter, a part that is left out by most other publications. First results indicate that our approach gives good tracking results and allows to track pedestrians from a moving host in real time.