Sparse BEV Fusion with Self-View Consistency for Multi-View Detection and Tracking

Multi-View Multi-Object Tracking (MVMOT) is essential for applications such as surveillance, autonomous driving, and sports analytics. However, maintaining consistent object identities across multiple cameras remains challenging due to viewpoint changes, lighting variations, and occlusions, which often lead to tracking errors.Recent methods project features from multiple cameras into a unified Bird's-Eye-View (BEV) space to improve robustness against occlusion. However, this projection introduces feature distortion and non-uniform density caused by variations in object scale with distance. These issues degrade the quality of the fused representation and reduce detection and tracking accuracy.To address these problems, we propose SCFusion, a framework that combines three techniques to improve multi-view feature integration. First, it applies a sparse transformation to avoid unnatural interpolation during projection. Next, it performs density-aware weighting to adaptively fuse features based on spatial confidence and camera distance. Finally, it introduces a multi-view consistency loss that encourages each camera to learn discriminative features independently before fusion.Experiments show that SCFusion achieves state-of-the-art performance, reaching an IDF1 score of 95.9% on WildTrack and a MODP of 89.2% on MultiviewX, outperforming the baseline method TrackTacular. These results demonstrate that SCFusion effectively mitigates the limitations of conventional BEV projection and provides a robust and accurate solution for multi-view object detection and tracking.

Growing Deep Neural Network Considering with Similarity between Neurons

Deep learning has excelled in image recognition tasks through neural networks inspired by the human brain. However, the necessity for large models to improve prediction accuracy introduces significant computational demands and extended training times.Conventional methods such as fine-tuning, knowledge distillation, and pruning have the limitations like potential accuracy drops. Drawing inspiration from human neurogenesis, where neuron formation continues into adulthood, we explore a novel approach of progressively increasing neuron numbers in compact models during training phases, thereby managing computational costs effectively. We propose a method that reduces feature extraction biases and neuronal redundancy by introducing constraints based on neuron similarity distributions. This approach not only fosters efficient learning in new neurons but also enhances feature extraction relevancy for given tasks. Results on CIFAR-10 and CIFAR-100 datasets demonstrated accuracy improvement, and our method pays more attention to whole object to be classified in comparison with conventional method through Grad-CAM visualizations. These results suggest that our method's potential to decision-making processes.