Deep People Detection: A Comparative Study of SSD and LSTM-decoder
Bibliographic record
Abstract
In this paper, we present a comparative study of two state-of-the-art object detection architectures - an end-to-end CNN-based framework called SSD [1] and an LSTM-based framework [2] which we refer to as LSTM-decoder. To this end, we study the two architectures in the context of people head detection on few benchmark datasets having small to moderately large number of head instances appearing in varying scales and occlusion levels. In order to better capture the pros and cons of the two architectures, we applied them with several deep feature extractors (e.g., Inception-V2, Inception-ResNet-V2 and MobileNet-V1) and report accuracy, speed and generalization ability of the approaches. Our experimental results show that while the LSTM-decoder can be more accurate in realizing smaller head instances especially in the presence of occlusions, the sheer detection speed and superior ability to generalize over multiple scales make SSD an ideal choice for real-time people detection.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".