Three-Dimensional Multiobject Tracking Based on Voxel Masking Encoder and Deep Hashing Paradigm

In autonomous driving, accurate 3-D multiobject tracking (MOT) plays a key role in ensuring vehicle safety. However, due to the complexity of the environment, existing methods still face many challenges when dealing with long-distance objects, partial occlusions, and interference from similar categories. To tackle these challenges, we propose a 3-D MOT framework based on a voxel masking encoder (VME) and a deep hashing paradigm (DHP). We introduce a masking strategy that processes voxel features from near to far while maintaining feature sparsity, effectively capturing global contextual information between spatial features. Simultaneously, DHP is utilized to generate image hash codes and compute their hamming distance from the category hash codes. This process effectively distinguishes between object categories and thus avoids cross-category object dissociation. In addition, we propose a distance optimization matching (DOM) method that uses geometric dimensions and spatial distances to build a cost matrix, achieving more efficient and precise object associations. Results from experiments conducted on the KITTI dataset reveal that our framework delivers outstanding tracking performance, surpassing other advanced methods in tracking accuracy. The code is released at https://github.com/lsy-collab/VD-MOT