Motion Prediction Using Temporal Inception Module
Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different temporal scales for different length inputs. We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions. In this paper, we propose a Temporal Inception Module (TIM) to encode human motion. Making use of TIM, our framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths. The experimental results on standard motion prediction benchmark datasets Human3.6M and CMU motion capture dataset show that our approach consistently outperforms the state of the art methods.
Code (1)
Tasks
Autonomous DrivingHuman motion predictionmotion predictionPredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fast Fourier Inception Networks for Occluded Video Prediction
Video prediction is a pixel-level task that generates future frames by employing the historical frames. There often exist continuous complex motions, such as object overlapping and scene occlusion in video, which poses g…
PredictionVideo PredictionDevelopment of Human Motion Prediction Strategy using Inception Residual Block
Human Motion Prediction is a crucial task in computer vision and robotics. It has versatile application potentials such as in the area of human-robot interactions, human action tracking for airport security systems, auto…
Human motion predictionmotion predictionACTION-Net: Multipath Excitation for Action Recognition
Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relation…
Action RecognitionTemporal Action LocalizationMD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction
The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlat…
Graph SamplingManagementPredictionvalidRobust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model
Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various per…
EEGElectroencephalogram (EEG)Emotion Recognition