BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection

• URL: http://arxiv.org/abs/2207.02281v1
• Date: Tue, 5 Jul 2022 19:45:49 GMT
• Title: BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
• Authors: Asiegbu Miracle Kanu-Asiegbu, Ram Vasudevan, Xiaoxiao Du
• Abstract summary: BiPOCO is a Bi-directional trajectory predictor with POse COnstraints for detecting anomalous activities of pedestrians in videos. We introduce a set of novel compositional pose-based losses with our predictor and leverage prediction errors of each body joint for pedestrian anomaly detection.
• Score: 14.740178121212132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present BiPOCO, a Bi-directional trajectory predictor with POse COnstraints, for detecting anomalous activities of pedestrians in videos. In contrast to prior work based on feature reconstruction, our work identifies pedestrian anomalous events by forecasting their future trajectories and comparing the predictions with their expectations. We introduce a set of novel compositional pose-based losses with our predictor and leverage prediction errors of each body joint for pedestrian anomaly detection. Experimental results show that our BiPOCO approach can detect pedestrian anomalous activities with a high detection rate (up to 87.0%) and incorporating pose constraints helps distinguish normal and anomalous poses in prediction. This work extends current literature of using prediction-based methods for anomaly detection and can benefit safety-critical applications such as autonomous driving and surveillance. Code is available at https://github.com/akanuasiegbu/BiPOCO.

Related papers

• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments [43.28710918095309]
  Navigating complex and dynamic environments requires autonomous vehicles to reason about both visible and occluded regions. This involves predicting the future motion of observed agents, inferring occluded ones, and modeling their interactions. We introduce the Scene Informer, a unified approach for predicting both observed agent trajectories and inferring occludeds in a partially observable setting.
  arXiv  Detail & Related papers  (2023-09-25T06:16:09Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Patch-level Gaze Distribution Prediction for Gaze Following [49.93340533068501]
  We introduce the patch distribution prediction ( PDP) method for gaze following training. We show that our model regularizes the MSE loss by predicting better heatmap distributions on images with larger annotation variances. Experiments show that our model bridging the gap between the target prediction and in/out prediction subtasks, showing a significant improvement on both subtasks on public gaze following datasets.
  arXiv  Detail & Related papers  (2022-11-20T19:25:15Z)
• Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models [37.09462631862042]
  Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents. We propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents.
  arXiv  Detail & Related papers  (2022-10-26T09:39:31Z)
• Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection [14.740178121212132]
  We propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. Our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally. We present experimental results on real-world benchmark datasets on varying timescales and show that our proposed trajectory-predictor-based anomaly detection pipeline is effective and efficient.
  arXiv  Detail & Related papers  (2022-07-05T19:44:34Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory [14.725386295605666]
  We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework. We show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods. We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.
  arXiv  Detail & Related papers  (2022-03-14T13:39:44Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation [39.41917241231786]
  This paper addresses the problem of cascading errors by focusing on the coupling between the tracking and prediction modules. By using state-of-the-art tracking and prediction tools, we conduct a comprehensive experimental evaluation of how severely errors stemming from tracking can impact prediction performance. We show that this framework improves overall prediction performance over the standard single-hypothesis tracking-prediction pipeline by up to 34.2% on the nuScenes dataset.
  arXiv  Detail & Related papers  (2021-10-18T17:30:59Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.