SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task. Current evaluation metrics all require annotated ground truth, thus will fail in the test environment and realistic circumstances prohibiting further optimization after training. By contrast, our metric reflects the internal characteristics of trajectory hypotheses and measures tracking performance without ground truth. We demonstrate that trajectories with different qualities exhibit different single or multiple peaks over feature distance distribution, inspiring us to design a simple yet effective method to assess the quality of trajectories using a two-class Gaussian mixture model. Experiments mainly on MOT16 Challenge data sets verify the effectiveness of our method in both correlating with existing metrics and enabling parameters self-optimization to achieve better performance. We believe that our conclusions and method are inspiring for future multi-object tracking in practice.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Object TrackingObject TrackingSimilar Papers 제목 키워드 기반
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization
In this experience report, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations. We are interested in optimizing the design of structur…
Active LearningSelf-Supervised Learning of Iterative Solvers for Constrained Optimization
Obtaining the solution of constrained optimization problems as a function of parameters is very important in a multitude of applications, such as control and planning. Solving such parametric optimization problems in rea…
GPUSelf-Supervised LearningSelf-Tuning Sparse Attention: Multi-Fidelity Hyperparameter Optimization for Transformer Acceleration
Sparse attention mechanisms promise to break the quadratic bottleneck of long-context transformers, yet production adoption remains limited by a critical usability gap: optimal hyperparameters vary substantially across l…
Hyperparameter OptimizationOnline Iterative Self-Alignment for Radiology Report Generation
Radiology Report Generation (RRG) is an important research topic for relieving radiologist' heavy workload. Existing RRG models mainly rely on supervised fine-tuning (SFT) based on different model architectures using dat…
Reinforcement Learning (RL)Quality of syntactic implication of RL-based sentence summarization
Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models those input is enriched with part-of-speech (POS)-tags and dependency infor…
POSReinforcement LearningReinforcement Learning (RL)Sentence+1