Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos
This addresses distracted driving detection, a critical safety issue, but is incremental as it builds on existing contrastive learning methods with specific adjustments for video data.
The paper tackles the problem of detecting distracted driving behaviors from multimodal videos by proposing a supervised contrastive learning approach with modifications to the loss function and projection head, achieving ROC AUC improvements of 4.23% to 8.91% across modalities and a best AUC ROC of 0.9738.
Distracted driving is one of the major reasons for vehicle accidents. Therefore, detecting distracted driving behaviors is of paramount importance to reduce the millions of deaths and injuries occurring worldwide. Distracted or anomalous driving behaviors are deviations from 'normal' driving that need to be identified correctly to alert the driver. However, these driving behaviors do not comprise one specific type of driving style and their distribution can be different during the training and test phases of a classifier. We formulate this problem as a supervised contrastive learning approach to learn a visual representation to detect normal, and seen and unseen anomalous driving behaviors. We made a change to the standard contrastive loss function to adjust the similarity of negative pairs to aid the optimization. Normally, in a (self) supervised contrastive framework, the projection head layers are omitted during the test phase as the encoding layers are considered to contain general visual representative information. However, we assert that for a video-based supervised contrastive learning task, including a projection head can be beneficial. We showed our results on a driver anomaly detection dataset that contains 783 minutes of video recordings of normal and anomalous driving behaviors of 31 drivers from the various top and front cameras (both depth and infrared). Out of 9 video modalities combinations, our proposed contrastive approach improved the ROC AUC on 6 in comparison to the baseline models (from 4.23% to 8.91% for different modalities). We performed statistical tests that showed evidence that our proposed method performs better than the baseline contrastive learning setup. Finally, the results showed that the fusion of depth and infrared modalities from the top and front views achieved the best AUC ROC of 0.9738 and AUC PR of 0.9772.