Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Night-time vehicle detection is essential in building intelligent transportation systems (ITS) for road safety. Most of current night-time vehicle detection approaches focus on one or two classes of vehicles. In this paper, we present a novel multiclass vehicle detection system based on tensor decomposition and object proposal. Commonly used features such as histogram of oriented gradients and local binary pattern often produce useless image blocks (regions), which can result in unsatisfactory detection performance. Thus, we select blocks via feature ranking after tensor decomposition and only extract features from these selected blocks. To generate windows that contain all vehicles, we propose a novel object-proposal approach based on a state-of-the-art object-proposal method, local features, and image region similarity. The three terms are summed with learned weights to compute the reliability score of each proposal. A bio-inspired image enhancement method is used to enhance the brightness and contrast of input images. We have built a Hong Kong night-time multiclass vehicle dataset for evaluation. Our proposed vehicle detection approach can successfully detect four types of vehicles: 1) car; 2) taxi; 3) bus; and 4) minibus. Occluded vehicles and vehicles in the rain can also be detected. Our proposed method obtains 95.82% detection rate at 0.05 false positives per image, and it outperforms several state-of-the-art night-time vehicle detection approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it