Segmentasi dan pengesanan objek bergerak dalam keadaan cuaca berjerebu dan berkabus

Segmentation and detection of moving object are very important in navigation applications to improve visibility of computer vision technology. The challenges to these issues are how these two issues address hazy and foggy weather. This situation affects technology and specifically the video data used to detect moving objects. This problem occurs due to the light that is scattered because of the fog and haze pixels which prevent light from penetrating resulting in over segmentation. Various methods have been used to improve accuracy and sensitivity in over segmentation but further enhancement is needed to improve the performance in the detection of moving objects. In this research, a new method is proposed to overcome over segmentation which is a combination between Gaussian Mixture Model and other filters based on their own specialities. The combined filters comprised Median Filter and Average Filter for over segmentation, Morphology Filter and Gaussian Filter to rebuild structure element of pixel object, and combination of Blob Analysis, Bounding Box and Kalman Filter to reduce False Positive detection. The combination of these filters is known as Object of Interest Movement (OIM). Qualitative and quantitative methods were used to make comparison with previous methods. Data comprised sources of haze recordings obtained from YouTube and open dataset from Karlsure. Comparative analysis of pictures and calculations of detection of objects were done. Result showed that the combined filters is capable of improving accuracy and sensitivity of the segmentation and detection which were 72.24% for foggy videos, and 76.73% in hazy weather. Based on the findings, the OIM method has proven its capability to improve the accuracy of segmentation and detection object without the need for enhancement to contrast an image

Adaptive video defogging base on background modeling

The performance of intelligent video surveillance systems is always degraded under complicated scenarios, like dynamic changing backgrounds and extremely bad weathers. Dynamic changing backgrounds make the foreground/background segmentation, which is often the first step in vision-based algorithms, become unreliable. Bad weathers, such as foggy scenes, not only degrade the visual quality of the monitoring videos, but also seriously affect the accuracy of the vision-based algorithms. In this thesis, a fast and robust texture-based background modeling technique is first presented for tackling the problem of foreground/background segmentation under dynamic backgrounds. An adaptive multi-modal framework is proposed which uses a novel texture feature known as scale invariant local states (SILS) to model an image pixel. A pattern-less probabilistic measurement (PLPM) is also derived to estimate the probability of a pixel being background from its SILS. Experimental results show that texture-based background modeling is more robust than illumination-based approaches under dynamic backgrounds and lighting changes. Furthermore, the proposed background modeling technique can run much faster than the existing state-of-the-art texture-based method, without sacrificing the output quality. Two fast adaptive defogging techniques, namely 1) foreground decremental preconditioned conjugate gradient (FDPCG), and 2) adaptive guided image filtering are next introduced for removing the foggy effects on video scenes. These two methods allow the estimation of the background transmissions to converge over consecutive video frames, and then background-defog the video sequences using the background transmission map. Results show that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the reliable foreground/ background segmentation results, the foreground transmissions can then be recovered by the proposed 1) foreground incremental preconditioned conjugate gradient (FIPCG), or 2) on-demand guided image filtering. Experimental results show that the proposed methods can effectively improve the visual quality of surveillance videos under heavy fog and bad weathers. Comparing with state-of-the-art image defogging methods, the proposed methods are shown to be much more efficient.published_or_final_versionComputer ScienceDoctoralDoctor of Philosoph