BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation

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

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…

MUSCLE-NET：面向行人轨迹预测的预测多尺度感知网络

MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting

提出MUSCLE-NET，通过多尺度多模态特征提取和尺度自适应预测机制，解决现有方法对观测信息利用不足及忽视未来运动尺度依赖的问题，在JAAD和PIE数据集上取得竞争性能。

他们将去哪里？从自我中心视频建模多模态行人机动

Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos

提出MMPM框架，通过行为感知交互模块和基于CVAE的模态感知轨迹预测器，分别建模行人过马路和不过马路两种模式，提升自我中心视角下多模态轨迹预测准确性。

BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection

Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection. Most existing methods directly inject the TIR modality into the RGB-based framework or simply ensemble the results of two modalities. This, however, could lead to inferior detection performance, as the RGB and TIR features generally have modality-specific noise, which might worsen the features along with the propagation of the network. Therefore, this work proposes an effective and efficient cross-modality fusion module called Bi-directional Adaptive A…

HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grained tasks remains an open problem. In this study, we propose a new attention-based deep neural network, named as HydraPlus-Net (HP-net), that multi-directionally feeds the multi-level attention maps to different feature layers. The attentive deep features learned from the propose…

Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

The time it takes for a classifier to make an accurate prediction can be crucial in many behaviour recognition problems. For example, an autonomous vehicle should detect hazardous pedestrian behaviour early enough for it to take appropriate measures. In this context, we compare the switching linear dynamical system (SLDS) and a three-layered bi-directional long short-term memory (LSTM) neural network, which are applied to infer pedestrian behaviour from motion tracks. We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samp…