The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.

This dissertation presents an integrated human shape modeling, detection, and body part localization vision system. It demonstrates that the system can (1) detect pedestrians in various shapes, sizes, postures, partial occlusion, and clothing from a moving vehicle using stereo cameras; (2) locate the joints of a person automatically and accurately without employing any markers around the joints.

The detection of objects in every frame of a sequence is often not sufficient for scene interpretation. Tracking can increase the robustness, especially when occlusions occur or when objects temporally disappear. The standard approach for tracking is to use a Kalman filter for every object. This, however, requires the use of a high complexity management system to deal with the multiple hypotheses necessary to track all anticipated objects.

Transit buses are involved in many more accidents than other vehicles. Collision Warning Systems (CWS) are therefore placed most efficiently on these buses. In our project, we investigate their operating environment and available technologies to develop performance specifications for such CWS. This paper discusses our findings of transit buses driving through very cluttered surroundings and being involved in many different types of accidents where currently available CWS do not work effectively. One of the focuses of our work is pedestrians around the bus and their detection. Keywords collision avoidance, transit bus, safety, sensors. 1 Transit Bus Side Collisions The goal of our project is to build intelligent vehicle systems to reduce side collisions in transit buses. Transit buses are already a very safe mode of transportation: the passenger-fatality rate per passenger-mile is about 15 times smaller than the equivalent rate for other vehicles [1]. The sheer size of a bus helps to...

Extending a differential total least squares method for range flow estimation we present an iterative regularisation approach to compute dense range flow fields. We demonstrate how this algorithm can be used to detect motion discontinuities. This can can be used to segment the data into independently moving regions. The different types of aperture problem encountered are discussed. Our regularisation scheme then takes the various types of flow vectors and combines them into a smooth flow field within the previously segmented regions. A quantitative performance analysis is presented on both synthetic and real data. The proposed algorithm is also applied to range data from castor oil plants obtained with the Biris laser range sensor to study the 3-D motion of plant leaves.

Detection and tracking of moving objects (DATMO) in crowded urban areas from a ground vehicle at high speeds is difficult because of a wide variety of targets and uncertain pose estimation from odometry and GPS/DGPS. In this paper we present a solution of the simultaneous localization and mapping (SLAM) with DATMO problem to accomplish this task using ladar sensors and odometry. With a precise pose estimate and a surrounding map from SLAM, moving objects are detected without a priori knowledge of the targets. The interacting multiple model (IMM) estimation algorithm is used for modeling the motion of a moving object and to predict its future location. The multiple hypothesis tracking (MHT) method is applied to refine detection and data association. Experimental results demonstrate that our algorithm is reliable and robust to detect and track pedestrians and different types of moving vehicles in urban areas.

Image sequences capturing Hurricane Luis through meteorological satellites (GOES-8 and GOES-9) are used to estimate hurricane-top heights (structure) and hurricane winds (motion). This problem is difficult not only due to the absence of correspondences but also due to the lack of depth cues in the 2D hurricane images (scaled orthographic projection). In this paper, we present a structure and motion analysis system, called SMAS. In this system, the hurricane images are first segmented into small square areas. We assume that each small area is undergoing similar nonrigid motion. A suitable nonrigid motion model for cloud motion is first defined. Then, non-linear least-square method is used to fit the nonrigid motion model for each area in order to estimate the structure, motion model, and 3D nonrigid motion correspondences. Finally, the recovered hurricane-top heights and winds are presented along with an error analysis. Both structure and 3D motion correspondences are estimated to subpi...

This paper proposes a new method for recovering nonrigid motion and structure of clouds under affine constraints using time-varying cloud images obtained from meteorological satellites. This problem is challenging not only due to the correspondence problem but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In this paper, affine motion is chosen as a suitable model for small local cloud motion. However, the cloud motion model can also be extended and not necessarily be affine. Analysis of intensity images is first used to find the candidates of correspondence for each point. Then affine motion model is utilized to find the structure (cloud-top height), motion parameters, and point correspondences. Extensive analysis has been done on defining appropriate constraints on depth that are necessary for achieving converge. Finally, experimental results on time-varying images of hurricane Luis, captured by GOES-9 satellite in geosynchronous orbit are...

The realization of on- and o#-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured environment, the UGVs should have the di#cult and computationally intensive competency to predict the future locations of moving objects that could interfere with its path. This paper details the development of a combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure. The development of a moving object testbed that facilitates the testing of di#erent representations and prediction algorithms in an implementation-independent platform is also outlined.

The detection of objects in every frame of a sequence is often not sufficient for scene interpretation. Tracking can increase the robustness, especially when occlusions occur or when objects temporarily disappear. In this paper we present a stochastic tracking approach which is based on the CONDENSATION algorithm -- conditional density propagation over time -- that is capable of tracking multiple objects with multiple hypotheses in range images. A probability density function describing the likely state of the objects is propagated over time using a dynamic model. The measurements influence the probability function and allow the incorporation of new objects into the tracking scheme. Additionally, the representation of the density function with a fixed number of samples ensures a constant running time per iteration step. Results with data from different sources are shown for automotive applications. 1.