Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Algorithm 1

Training density map guided detection for RGBD-Net/RGBT-Net.

	Input: Training density map image § = {D1, …, DN}, training
	Epochs Nci, and Adaptive Gaussian kernel initialization input initial mean μ = 0.5 and standard deviation σ = 0.02
	Onput: A dense crowd detection model with parameters θ AND crowd head detection box
	Initializing density map image D and parameters θ
	for each epoch do
	Step 1:
	If (h == hthermal)
	Gauss kernel matching stop;
	else Continue to match head size according to (5);
	If (h == hDepth)
	Gauss kernel matching stop;
	else Continue to match head size according to (6);
	Step 2: Multivariate Gauss matching with all head sizes;
	Step 3: Multimode features for midterm fusion, according to (9–17);
	Step 4: If (If (8) is true)
	Generating density maps with adaptive Gaussian kernel constraints;
	else Regenerate Gaussian density map according to (7)
	Step 5: Density map guided generation of crowd head detection box;
	Step 6: Update D according to (4).
	end