HLAC Approach to Automatic Object Counting

Counting (identical) objects in images is a simple yet fundamental recognition task that requires exhaustive human effort. Automation of this task would reduce the human load significantly. In this paper, we propose a statistical method to automatically count objects in an image sequence by using Higher-order Local Auto-Correlation (HLAC) based image features and Multiple Regression Analysis (MRA). This method is based on a simple computation, which enables fast and automatic object counting in real time. We propose several methods that have different preprocessing and image features and conduct comparative experiments of counting objects (ducks in this paper) in images captured by outdoor monitoring cameras. The experimental results demonstrated the effectiveness of the proposed methods.

• Type: Conference
• Year: 2008
• Where: BLISS
• Authors: Takumi Kobayashi, Tadaaki Hosaka, Shu Mimura, Takashi Hayashi, Nobuyuki Otsu
• Keywords: BLISS 2008, Exhaustive Human Effort, Image Features, Images, Machine Learning