Flow-Count Network for Video Crowd Analysis
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Solution Overview
Problem
Existing video crowd counting methods face challenges in accuracy and efficiency, particularly in highly crowded scenes, due to the reliance on costly annotation and computation, inefficient use of temporal information, and errors caused by irrelevant objects in optical flow, with existing flow-based networks being inefficient and requiring scene-dependent hyperparameter tuning.
Innovation Solution
The proposed system extracts spatial and temporal features from video frames to construct pixel-level flow maps and density maps, using a Flow-Count network that learns people flow information for improved counting accuracy, filters out irrelevant data, and reduces computational cost by disabling flow estimation during inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If tracking-based methods are used for video crowd counting, then counting can be performed using bounding boxes, but accuracy degrades quickly under highly crowded conditions and annotation/computation costs increase
Solution Approach 1:
The patent extracts only the necessary temporal information (flow maps) from video sequences, discarding redundant tracking and annotation steps. By focusing on flow-based representation rather than full object tracking, the method reduces annotation burden while maintaining counting accuracy in crowded scenes.
Solution Approach 2:
The patent replaces the mechanical tracking system (which requires bounding box annotation and individual object following) with a flow-based field representation. This substitution uses optical flow fields to capture crowd motion patterns without requiring precise object boundaries or identities, reducing annotation costs while preserving counting accuracy.
2Measurement precision
If video optical flow is used as input for flow-based networks, then people flow information can be captured, but inference efficiency decreases due to the delay of tandem design
Solution Approach 1:
The patent performs flow map construction as a preliminary step during training, learning to directly predict flow maps from video frames. During inference, the pre-trained network directly generates flow maps without requiring external optical flow estimation, eliminating the tandem design delay while preserving flow information extraction accuracy.
Solution Approach 2:
The patent merges the optical flow estimation and crowd counting tasks into a single unified network. The network simultaneously learns to extract flow information and perform counting, eliminating the need for separate optical flow estimation modules and reducing inference time while maintaining flow extraction accuracy.
3Measurement precision
If grid-based flow representation is used, then flow between adjacent grids can be analyzed, but scene-dependent hyperparameter tuning is required
Solution Approach 1:
The patent develops a universal flow map representation that works across different crowd scenes without requiring scene-specific grid configurations. The learned flow maps adapt to various crowd densities and movement patterns automatically, eliminating the need for hyperparameter tuning while maintaining flow analysis precision across diverse scenarios.
Data Source
AI summary
A system for flow-count includes one or more cameras and a processing system. The one more cameras are configured to capture image data comprising a sequence of frames. The processing system is configured to extract spatial features and temporal features based on the sequence of frames; construct, based on the spatial features and temporal features, a density map and/or a flow map corresponding to a frame of the sequence of frames, wherein the density map comprises density values for the plurality of pixels, wherein the flow map comprises two-dimensional (2D) vectors for the plurality of pixels, and wherein a 2D vector indicates a speed and direction for a pixel of the plurality of pixels in the frame; and determine, based on the density map and/or the flow map, a count, a distribution, and/or movement of objects corresponding to the frame.