Nicolai Wojke

Moving vehicle detection and tracking in unstructured environments

The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable in urban environments. To overcome the unavailability of road network information for background separation, we introduce a foreground model that incorporates geometric as well as temporal cues. Local shape estimates successfully guide vehicle localization. Extensive evaluation shows that the system works reliably and efficiently in various outdoor scenarios without any prior knowledge about the road network. Experiments with our own sensor as well as on publicly available data from the DARPA Urban Challenge revealed more than 96% correctly identified vehicles.

Probabilistic terrain classification in unstructured environments

Autonomous navigation in unstructured environments is a complex task and an active area of research in mobile robotics. Unlike urban areas with lanes, road signs, and maps, the environment around our robot is unknown and unstructured. Such an environment requires careful examination as it is random, continuous, and the number of perceptions and possible actions are infinite. We describe a terrain classification approach for our autonomous robot based on Markov Random Fields (MRFs ) on fused 3D laser and camera image data. Our primary data structure is a 2D grid whose cells carry information extracted from sensor readings. All cells within the grid are classified and their surface is analyzed in regard to negotiability for wheeled robots. Knowledge of our robots egomotion allows fusion of previous classification results with current sensor data in order to fill data gaps and regions outside the visibility of the sensors. We estimate egomotion by integrating information of an IMU, GPS measurements, and wheel odometry in an extended Kalman filter. In our experiments we achieve a recall ratio of about 90% for detecting streets and obstacles. We show that our approach is fast enough to be used on autonomous mobile robots in real time.

Confidence-based pedestrian tracking in unstructured environments using 3D laser distance measurements.

Detection and tracking of pedestrians is an essential task for autonomous outdoor robots. Modern 3D laser range finders provide a rich and detailed 360 degree picture of the environment. Unstructured environments pose a difficult scenario where a variety of objects with similar shape to a human like shrubs or small trees occur. Especially in combination with partial occlusions, sensor noise, and conclusions from traversing rough terrain.

Global data association for the Probability Hypothesis Density filter using network flows

The Probability Hypothesis Density (PHD) filter is an efficient formulation of multi-target state estimation that circumvents the combinatorial explosion of the multi-target posterior by operating on single-target space without maintaining target identities. In this paper, we propose a multi-target tracker based on the PHD filter that provides instantaneous state estimation and delayed decision on data association. For this purpose, we reformulate the PHD recursion in terms of single-target track hypotheses and solve a min-cost flow network for trajectory estimation where measurement likelihoods and transition probabilities are based on multi-target state estimates. In this manner, the presented approach combines global data association with efficient multi-target filtering. We evaluate the approach on a publicly available pedestrian tracking dataset to present state estimation and data association capabilities.

Localization and pose estimation of textureless objects for autonomous exploration missions

In this paper we describe an approach for detection and pose estimation of colored objects with only few or no textural features. The approach consists of two separate stages. First, we perform vision-based object detection and hypothesis filtering. Then, we estimate and validate the objects pose in 3-D laser scans. For object detection we integrate image segmentation results from multiple viewpoints in a set-theoretical filter that provides a probabilistically sound estimate of the number of objects and their respective locations. For validation and pose estimation we search for the best pose by sampling from a geometric measurement model. The system has been validated during autonomous exploration missions in unstructured and space-like environments.

Detecting Fine-Grained Affordances with an Anthropomorphic Agent Model

In this paper we propose an approach to distinguish affordances on a fine-grained scale. We define an anthropomorphic agent model and parameterized affordance models. The agent model is transformed according to affordance parameters to detect affordances in the input data. We present first results on distinguishing two closely related affordances derived from sitting. The promising results support our concept of fine-grained affordance detection.

Gaze-estimation for consumer-grade cameras using a Gaussian process latent variable model

Commercial gaze-tracking devices provide accurate measurements of the visual gaze and are applied to a broad range of problems in marketing, human-computer interaction, and health care technology. In some applications commercial systems are either unavailable or unaffordable. Therefore, developing low cost solutions using off the shelf components is worthwhile. In the paper at hand, we apply a hierarchy of Gaussian processes, a class of probabilistic function regressors, to the problem of visual gaze-tracking for consumer grade cameras. Gaussian process latent variable models lead to a lower dimensional manifold which represents the gaze space. Finally, a Gaussian process mapping from screen coordinates to gaze manifold enables us to seek for the users visual gaze point given a previously unseen eye-patch. In our experiments, we achieve mean errors of approximately 2 cm for a consumer grade webcam that is positioned 30-40 cm in front of the user.

Joint operator detection and tracking for person following from mobile platforms

In this paper, we propose an integrated system to detect and track a single operator that can switch off and on when it leaves and (re-)enters the scene. Our method is based on a set-valued Bayes-optimal state estimator that integrates RGB-D detections and image-based classification to improve tracking results in severe clutter and under long-term occlusion. The classifier is trained in two stages: First, we train a deep convolutional neural network to obtain a feature representation for person re-identification. Then, we bootstrap a classifier that discriminates the operator from remaining people on the output of the state-estimator. We evaluate the approach on a publicly available multi-target tracking dataset as well as custom datasets that are specific to our problem formulation. Experimental results suggest reliable tracking accuracy in crowded scenes and robust re-detection after long-term occlusion.

Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking.

The Probability Hypothesis Density Filter (PHD) filter is an efficient recursive multi-object state estimator that systematically deals with data association uncertainty. In this paper, we apply the PHD filter in a tracking-bydetection framework. In order to mimic state-dependent false alarms, we introduce an adapted PHD recursion that defines clutter generators in state space. Further, we integrate detector confidence scores into the measurement likelihood. This extension is quite effective yet simple, which means that it requires few changes to the original PHD recursion, that it has the same computational complexity, and that there exist few parameters that must be adapted to the individual tracking scenario. Our evaluation on a popular pedestrian tracking dataset demonstrates results that are competitive with the state-of-the-art.