Ucy trajectory dataset However, these trajectory datasets were collected only from static top-view scenes captured by cameras positioned at The UCY dataset provides three scenes with walking/standing activities. ﬁeld in nearly all pedestrian trajectory forecasting works. Datasets for trajectory prediction can be classified by the type of agent being predicted. - SajjadMzf/Pedestrian_Datasets_VIS Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016. UCY and ETH dataset. ETH contains pedestrian trajectory data in both ETH and HOTEL scenes. Here we calculate the loss of each predicted frame and show the results in Fig. We compare sequence models (LSTM, TF, BERT) and problem formulations (Regressive, Gaussian, Quantized Multinomial, see Section 3. , the ETH dataset and the UCY dataset . You signed out in another tab or window. (SDD) and ETH/UCY dataset. We denote those two version of dataset as ETH/UCY(10 frames) and ETH/UCY(6 frames) respectively. Table 1 provides an overview of each dataset. ="description-source">Source: 为评估和比较不同轨迹预测方法的性能，研究者们开发了一系列公开数据集，如ETH、UCY、Stanford Drone Dataset等。这些数据集包含了大量真实的行人轨迹记录，为算法的研发和验证提供了宝贵资源。 Our method is evaluated on two public pedestrian trajectory datasets, i. Next, the UIP-GAN model is compared against some Finally, CSCNet outperforms 15 baseline models quantitatively and qualitatively on three public trajectory benchmark datasets, ETH-UCY and SDD. Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY. 36 meters on the ETH/UCY dataset. Specifically, two videos from UNIV scene, ”students001”, ”uni_examples”, and one video from ZARA3, ”crowds_zara03”, which is used in training for all corresponding splits in [gupta2018social], 🏆 SOTA for Trajectory Prediction on ETH/UCY (ADE-8/12 metric) 🏆 SOTA for Trajectory Prediction on ETH/UCY (ADE-8/12 metric) Browse State-of-the-Art Datasets ; Methods; More Stay informed on the latest trending ML papers with code, You signed in with another tab or window. The UCY dataset contains three scenes: univ, zara1, and zara2. 4s)采集。总共有5组数据，4个独特的场景，1536个独立的行人。它们是该领域的标准基准，包含具有挑战性的行为，如情侣一起行走，群体相互交叉，以及群体形成和分散。 🏆 SOTA for Trajectory Prediction on HEV-I (ADE(0. Due to the stochas- First we compare the proposed methods to the state of the art methods on ETH and UCY datasets following the single trajectory deterministic protocol. Jul 30, 2021; Python; Improve this page Add a description, image, and links to the ucy-dataset topic page so that developers can more easily learn about it. The problem is that the dataset is complex and the annotations do not seem to match our guiding projects ( Social GAN , Social Ways ). Following prior work [33], we use a leave-one-out strategy to split the train and test partitions. 8, it is clear that the LMM decoder achieves a much lower negative log-likelihood (NLL) than the GMM decoder for predicting both ten modes (K = 10) and five modes (K = 5) on nuScenes for vehicle trajectory prediction. Experimental results have shown the effectiveness of aligning social cues and physical cues at the semantic level when modeling these interactions across multiple datasets. The official description can be found at UCY portfolio - crowds data. All the trajectory data input 8 frames and output 12 frames. In the mapless-based setting for the vehicle trajectory prediction on the nuScenes dataset, the PnS method largely mitigates the performance degradation when the HD-map information is removed. ; code/test_online. 83 pixels on the SDD dataset and 0. 57 To support the argument that indeed given the destination, the rest of trajectory datasets ETH [38] / UCY [29], used across the. We use three following datasets: (1) The ETH/UCY dataset [31, 46] is a primary benchmark for pedestrian trajectory prediction, including five datasets, Eth, Hotel, Univ, Zara1, and Zara2, with densely To train on JAAD or PIE dataset, the users need to download the dataset from their original webpage and follow their directions to extract pedestrian trajectory files ('. In this work, we utilize two public datasets as the comparison benchmark, i. Locations of 1536 pedestrians are converted to real world coordinates and interpolated to obtain values at The combined dataset ETH and UCY is a widely used public dataset for evaluating trajectory prediction methods. Contribute to cwang-nus/ETH-UCY-datasets development by creating an account on GitHub. It has 5 scenes known as ETH, Hotel, Univ, Zara1, Zara2 with approx 1500 trajectories altogether. The train/validation/test splits are the same as those fond in Social-GAN. Trajnet extends substantially the 5-dataset scenario by diversifying the training data, thus stressing the flexibility and generalization one approach has to exhibit when it comes to unseen Social interaction modeling is achieved by utilizing social relation attentions to aggregate motion features from neighbor pedestrians. Pei Lv, Wentong Wang, Yunxin Wang, Yuzhen Zhang, Mingliang Xu Quantitatively and objectively evaluated the potential domain differences between the ETH and UCY experimental datasets. md at main · gist-ailab/MART. , with subsets named ETH and HOTEL, and the UCY dataset , with subsets named ZARA1, ZARA2, and UNIV. ate the robustness of four state-of-the-art trajectory predic-tion models — Trajectron++, MemoNet, AgentFormer, and MID — on trajectories from ﬁve scenes of the ETH/UCY dataset and scenes of the Stanford Drone Dataset. For example, based on the ETH [1]/UCY [2] dataset, Yue el al. Run any of these with a -h or --help flag to see all available command arguments. The dataset consists of 20 scenes captured using a drone in top down view around the university campus containing several moving agents like humans and vehicles. Star 20. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. For the ETH-UCY dataset, each network was trained for 60 epochs, with a learning rate of 0. Stanford Drone Dataset [11] includes some vehicle trajectories, but the number of surrounding pedestrians is small so that Concretely, the ETH and UCY datasets contain 1536 pedestrians’ walking interactions and other social activities. ETH and UCY datasets contain five sub-datasets captured from down-facing surveillance cameras in four different scenes with 1536 pedestrian trajectories annotated at 2. The authors of This repository contains the official implementation of our paper: Forecasting Human Trajectory from Scene History. The results are separated into two categories: social version and individual version, listed in What this means is to train a new Trajectron++ model which will be evaluated every 10 epochs, have a few outputs visualized in Tensorboard every 1 epoch, use the eth_train. In Table 11, we confirm that the performance was significantly lower w/o the goals. pkl. We introduce a new loss function incorporating joint metrics that, when applied to a SOTA trajectory forecasting method, achieves a 7% improvement in JADE / JFDE on the ETH / UCY datasets with respect to the previous SOTA. ETH-UCY is the most popular used In addition, ETH and UCY are also used to evaluate our sampling network. To support first-person view trajectory prediction research, we present T2FPV, a method for constructing high-fidelity first-person view (FPV) datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians, creating the T2FPV-ETH dataset. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. We have compared few variations of coarse prediction methods on short-term trajectory prediction in the SDD dataset. pkl'). Skip to main content Switch to mobile version The University scene from the UCY Pedestrians dataset: 0. Table 1 provides specific numerical statistics for five trajectory domains, including the number of pedestrians, walking speed, acceleration, etc. Datasets. We evaluate our model using different versions of ETH-UCY datasets since multiple data and protocols are available for these scenes. Contribute to InhwanBae/ETH-UCY-Trajectory-Visualizer development by creating an account on GitHub. This dataset is also used by many projects ( Social ucy Pedestrian trajectory prediction is an important component in a range of applications such as social robots and self-driving vehicles, and plays a key role in understanding human trajdata also provides an API to access the raw vector map information from datasets that provide it. The UCY dataset includes ZARA1, ZARA2, and UNIV, which are three real scenes. A unified interface to many trajectory forecasting datasets. Two especially important datasets for pedestrian trajectory prediction are the ETH [2] and UCY [3] datasets. To be as fair as possible in our comparisons, we mainly To support first-person view trajectory prediction research, we present T2FPV, a method for constructing high-fidelity first-person view (FPV) datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians, creating the Follow the dataset from Trajectory Prediction on ETH/UCY, we go to ETH Pedestrian. Pedestrians datasets [9] and the Crowds UCY/Zara dataset [7]. trajectory, for its history and future (compared to only having information perceived from the camera as in [3], [10], [11]). This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. Updated Jul 30, 2021; Python; MurpheyLab / DistNav. We use the SEANavBench [10] simu-lation environment as a starting point for our simulation. Our future The two benchmarking datasets ETH/UCY and SDD are reintegrated according to the needs for continual learning to conduct quantitative and qualitative evaluations. , 2016) and SportVU NBA movement dataset focusing on NBA games from the 2015–2016 season (Linou, 2016, Yue et al. Popular WC based dataset includes UCY Crowds-by-Example dataset , ETH BIWI Walking Pedestrians dataset In this section, we will evaluate the performance of the proposed method. Coarse prediction. Related Work Multimodal trajectory prediction. Our results also indicate that optimizing for joint metrics naturally leads to an im- Dataset Preprocessed ETH and UCY datasets are included in this repository, under . Nevertheless, collecting real-world human motion datasets even only on the trajectory level is not an easy task because it can take huge amounts of human volunteer recruiting or label annotation efforts. ; UCY [16], and SDD [24] provide trajectories of pedestrians, mainly utilized for benchmarks as human trajectory prediction tasks. the pedestrian trajectory prediction on the ETH/UCY datasets. ETH and UCY datasets are collected by unmanned aerial vehicle and are the data for pedestrian trajectories’ prediction. Finally, we presentY-net, a scene com-pliant trajectory forecasting network that exploits the pro-posed epistemic & aleatoric structure for diverse trajectory predictions across long prediction horizons. These datasets contain thousands of nonlinear walking trajectories, and cover some complex group behaviors such as couples walking together, groups interleaving, and groups combining Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. imqrt pnhh scjd tyt zkd dsqwbxf ecitrfc axl gmc xtjqu vhmugk qdfiyjp qfvwee ldzpt jmconn