A Biological Hierarchical Model Based Underwater Moving Object Detection

Abstract

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

“…A distance measure for decision making and a model update scheme is discussed in [135]. This approach was found useful in applications involving underwater videos [136], [137]. Local binary similarity patterns (LBSP) [172] are a variation of LBP and include spatio-temporal binary similarity metric.…”

Underwater image processing and analysis: A review

“…Xu et al [35] developed a novel approach for underwater target recognition in view of the generalized robust principal component analysis (GRPCA), which could extract the visual feature information from underwater images and was of great significance to the recognition and representation of subordinate images. Shen et al [51] explored a useful underwater target layered background framework on account of a frog eye visual information perception and processing scheme, which is able to separate salient objects from the image background with object contour. Based on underwater image feature extraction and matching, Zhang et al [52] developed a visual saliency detection model using a dark channel to remove haze.…”

Bionic Vision-Based Intelligent Power Line Inspection System

“…After a long period of evolution, biological visual systems develop a strong ability for sensing the world, and they help people's work and life in a variety of forms [ 12 ]. In [ 13 ], a biological hierarchical model based underwater moving object detection method was presented. In [ 14 ], a fly visual perception mechanism based small target detection method was proposed.…”