P2P Computer Vision AI model

نموذج رؤية حاسبيوة متقدم باستخدام تقنيات الـ P2P

التفاصيل التقنية المستخدمة في النموذج :

1. Technical Methodology and Mathematical Framework

1.1 Enhanced Multi-Threshold Detection System

To increase sensitivity across confidence levels, I introduced a multi-threshold detection cascade.

Multi-Threshold Detection Mathematics

D_final = ⋃{ D_t | t ∈ T }

T = {0.5, 0.3, 0.2, 0.1, 0.05}

D_t = { (x, y) | confidence(x, y) > t }

Enhanced Confidence Scoring

confidence_enhanced(x, y) = softmax(logits(x, y)) * spatial_weight(x, y)

This cascade captures detections at multiple confidence levels, reducing false negatives while preserving precision via intelligent post-processing.

1.2 DBSCAN-Based Crowd Density Analysis

In line with your recommendation to incorporate area-based calculations, I integrated DBSCAN clustering for crowd distribution analysis.

DBSCAN Mathematical Framework

DBSCAN(P, ε = 30, minPts = 3) → { C1, C2, …, Ck, N }

density(Ci) = |Ci| / area(convex_hull(Ci))

Enhancement Point Calculation

enhancement_points(Ci) = max(0, expected_count(Ci) − |Ci|)

expected_count(Ci) = density_baseline * area(Ci)

1.3 Gaussian Density Mapping

Density Map Generation

(x, y) = Σ_i G(x − x_i, y − y_i)G(x, y) = (1 / (2πσ²)) * exp(−(x² + y²) / (2σ²))

2. Implementation Architecture

2.1 Core Algorithm Implementation

Multi-Threshold Detection

multi_threshold_detection(self, outputs_scores, outputs_points):

thresholds = [0.5, 0.3, 0.2, 0.1, 0.05]

detections = {}

for threshold in thresholds:

mask = outputs_scores > threshold

points = outputs_points[mask].detach().cpu().numpy()

detections[threshold] = points.tolist()

return detections

DBSCAN Enhancement Integration

density_based_enhancement(self, points, img_shape):

clustering = DBSCAN(eps=30, min_samples=3).fit(points)

enhanced_points = self._apply_cluster_enhancement(points, clustering.labels_)

return enhanced_points

References

[1] Song, Q., et al. (2021). Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework. ICCV.

[2] Ester, M., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. KDD.