论文标题
Bipoco：双向轨迹预测，构成姿势限制的行人异常检测
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
论文作者
论文摘要
我们提出了Bipoco，这是一种带有姿势限制的双向轨迹预测指标，用于检测视频中行人的异常活动。与基于特征重建的先前工作相反，我们的工作通过预测他们的未来轨迹并将预测与他们的期望进行比较来确定行人异常事件。我们引入了一组新型的基于组成姿势的损失，并通过我们的预测因子和利用每个身体关节的预测误差来进行行人异常检测。实验结果表明，我们的Bipoco方法可以检测具有高检测率的行人异常活动（高达87.0％），并且纳入姿势限制有助于区分预测中的正常和异常姿势。这项工作扩展了使用基于预测的方法进行异常检测的当前文献，并可以使诸如自动驾驶和监视之类的安全至关重要应用受益。代码可从https://github.com/akanuasiegbu/bipoco获得。
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.
