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Driver Attention Monitoring
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Driver attention monitoring is the task of monitoring the attention of a driver.
<span style="color:grey; opacity: 0.6">( Image credit: Predicting Driver Attention in Critical Situations )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SecureGaze: Defending Gaze Estimation Against Backdoor Attacks
STDA: Spatio-Temporal Dual-Encoder Network Incorporating Driver Attention to Predict Driver Behaviors Under Safety-Critical Scenarios
AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction
Latent Embedding Clustering for Occlusion Robust Head Pose Estimation
Gated Driver Attention Predictor
FBLNet: FeedBack Loop Network for Driver Attention Prediction
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset
DADA: Driver Attention Prediction in Driving Accident Scenarios
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Predicting Driver Attention in Critical Situations
From Depth Data to Head Pose Estimation: a Siamese approach
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