Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images underperform due to the necessary view transformation of features from perspective view (PV) to a 3D world representation which is ambiguous due to missing depth information. This paper introduces X3KD, a comprehensive knowledge distillation framework across different modalities, tasks, and stages for multi-camera 3DOD. Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation. After the transformation, we apply cross-modal feature distillation (X-FD) and adversarial training (X-AT) to improve the 3D world representation of multi-camera features through the information contained in a LiDAR-based 3DOD teacher. Finally, we also employ this teacher for cross-modal output distillation (X-OD), providing dense supervision at the prediction stage. We perform extensive ablations of knowledge distillation at different stages of multi-camera 3DOD. Our final X3KD model outperforms previous state-of-the-art approaches on the nuScenes and Waymo datasets and generalizes to RADAR-based 3DOD. Qualitative results video at https://youtu.be/1do9DPFmr38.

All modern emotion theoretical views assume a role for peripheral physiological changes during emotional experiences. In this paper, we explored the correlation between autonomically-mediated changes in multimodal bodily signals and discrete emotional states. In order to fully exploit the information in each modality, week learners based on individual signal modalities are built and then fused to formed a robust inference model. To validate our model, three specific physiological signals including Electromyogram (EMG), Blood Volume Pressure (BVP) and Galvanic Skin Response (GSR) recorded during eight emotional states were analyzed. Our approach showed 88.1% emotion recognition accuracy, which outperformed the conventional Support Vector Machine (SVM) classifier with 17% accuracy improvement. Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner. Despite the feature space dimensionality reduction from 27 to 18 features, our methodology preserved the recognition accuracy of about 85.0%.

Automatic tracking of rodents' behaviors over time in their home cages is of great interest in psycho-physiological studies. The commercially-available animal monitoring systems use RGB videos or bio-potential signals to monitor behaviors of animals when exploring their surroundings. The based models of these devices starts from several thousands of dollars and the cost would increase if extra analysis features were added. In this study, we present a low-cost, non-contact animal tracking system which records depth data from the caged rodent to detect the animal's location and pose over time. An adaptive Gaussian Mixture Model (GMM) algorithm is employed to detect animal's center of mass and extract its movement trajectory over an extended period of time. The animal's pose is determined by applying Principle Component Analysis (PCA) on 3D depth data of the located animal. In conjunction with our previously-introduced respiratory detection algorithm, this system can be utilized as an automatic long-term and unobtrusive monitoring system for animal experiments. We validated the tracking accuracy of our system by monitoring two different caged voles. The voles' locations were correctly detected in 80% of times, while the poses were detected correctly in 100% of times confirmed by visually inspecting the color-coded depth videos.