The Ko-PER intersection laserscanner and video dataset

Strigel, Elias; Meißner, Daniel; Seeliger, Florian; Wilking, Benjamin; Dietmayer, Klaus

doi:10.1109/itsc.2014.6957976

Cited by 83 publications

(51 citation statements)

References 7 publications

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“…As for the surveillance data sets, we adopted a semiautomatic labeling approach where we first detected the actor in every frame, then computed the ground‐truth heading direction based on the derivative of the subject's position over a sequence of consecutive time frames. For the car HDE we used two surveillance data sets, VIRAT, and Ko‐PER (Strigel, Meissner, Seeliger, Wilking, & Dietmayer, ), in addition to one driving data set, PKU‐POSS (C. Wang et al, ). Table summarizes our data.…”

Section: Resultsmentioning

confidence: 99%

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Bonatti

Wang

et al. 2020

Journal of Field Robotics

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Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (a) vision-based target estimation; (b) 3D signed-distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time-horizon camera motion; and (d) learning-based artistic shot selection.We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU. K E Y W O R D S aerial robotics, cinematography, computer vision, learning, mapping, motion planning Within the filming context, this cost function measures jerkiness of motion, safety, environmental occlusion of the actor, and shot quality (artistic quality of viewpoints). This cost function depends on the environment and , and on the actor forecast ξ a , all of which are sensed on-the-fly. The changing nature of the environment and ξ a demands replanning at a high frequency. Here we briefly touch upon the four components of the cost function J(ξ q ) (refer to Section 7 for details and mathematical expressions): Smoothness J smooth (ξ q ): Penalizes jerky motions that may lead to camera blur and unstable flight; Safety J obs (ξ q , ): Penalizes proximity to obstacles that are unsafe for the UAV; Occlusion J occ (ξ q , ξ a , ): Penalizes occlusion of the actor by obstacles in the environment; Shot quality J shot (ξ q , ξ a , Ω art ): Penalizes poor viewpoint angles and scales that deviate from the desired artistic guidelines, given by the set of parameters Ω art . In its simplest form, we can express J(ξ q ) as a linear composition of each individual cost, weighted by scalars λ i . The objective is to * = ( ) ()={ } J J J J J J xyz J J J J J J x y z J occ + J obs 99.4 ± 2.2 94.2 ± 7.3 86.9 ± 9.3 J obs 98.8 ± 3.0 87.1 ± 8.5 75.3 ± 11.8 Avg. dist. to ξ shot (m) J occ + J obs 0.4 ± 0.4 6.2 ± 11.2 10.7 ± 13.2 J obs 0.05 ± 0.1 0.3 ± 0.2 0.5 ± 0...

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Section: Resultsmentioning

confidence: 99%

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Bonatti

Wang

et al. 2020

Journal of Field Robotics

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“…While the highway problem is highly studied, there is less focus on intersections, which account for a significant proportion of accidents -around 40% [2]. Intersection datasets include the Ko-PER project, but this is taken from an overhead perspective to simulate smart infrastructure [27]. As previously mentioned, NGSIM dataset Lankershim and Peachtree are also often used, and this is data collected from an overhead camera passed through a tracking algorithm.…”

Section: Related Workmentioning

confidence: 99%

Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

Zyner

Worrall

Nebot

2020

IEEE Trans. Intell. Transport. Syst.

179

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Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.

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“…So, each cell of grid matrix C represents a part of the intersection. The sample grids for the values 20 and 40 of step parameter for Ko-PER dataset intersection [27] are shown in Figure 3. As can be seen with a smaller step parameter, the grid matrix has more cells with a smaller size.…”

Section: Figure 1 Motion Directions Of Each Side For a Sample Intersmentioning

confidence: 99%

“…Afterwards, each user is tracked by key points inside the detected region. In the Ko-PER project, a dataset for intersection monitoring has been released [27]. This dataset contains laser-scanners and video cameras information.…”

Section: Introductionmentioning

confidence: 99%

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

Delavarian

Marouzi

Hassanpour

2020

IJE

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Visual vehicle tracking is an important topic in intelligent transportation systems. Intersections are challenging locations for visual systems to track vehicles which are simultaneously moving in different directions. In addition, normal traffic flow may change at intersections due to accidents. Congestion, occlusion and undetermined motion flows are the nominated challenging issues of vehicle tracking at intersections. In this paper, a method for tracking multiple vehicles is proposed considering the vehicle motion directions to overcome undetermined motion flows. For this purpose, a multilayer model is presented, which assigns each motion flows to distinct layers. Moreover, we introduce different neighborhoods for various layers considering the regular motion flows in a layer. Hence, vehicles entering from the same side of intersection with the same motion direction are assigned to the same layer. Then the tracking is performed on different layers separately. In special cases such as vehicles crossing each other, misdetections or occlusion, the proposed tracking method can predict the vehicles tracks by using the stored tracking history and considering neighborhoods in that layer. Experimental results show consistency between proposed tracking method results and ground truth, also outperformance of other tracking methods in tracking vehicles crossing the intersection.

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The Ko-PER intersection laserscanner and video dataset

Cited by 83 publications

References 7 publications

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

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