تشخيص تغييرات صحنه به روش زمينه گيري هوشمند
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه علم و صنعت ایران
2 - جهاد دانشگاهی صنعتی شريف
کلید واژه: بلوك بندي تصاويرتركيبي از گوسين‌هاتفاضل زمينهجداسازي رو زمينه,
چکیده مقاله :
جداسازي روزمينه از زمينه، در بسياري از كاربردهاي نظارت تصويري به عنوان اولين و مهمترين قدم شناخته ميشود و "تفاضل زمينه" روشي است كه معمولاً براي اين منظور استفاده ميشود. اين روش، هر فريم را با مدلي از صحنة خالي مقايسه كرده و ناحيههايي از آنرا كه بطور قابل ملاحظهاي متفاوت به نظر ميرسند به عنوان نواحي روزمينه مشخص ميكند. اين مقاله روش جديدي براي تفاضل زمينه ارائه ميكند كه در آن ابتدا هر تصوير به بلوكهاي يكساني تفكيك شده و سپس ويژگيهاي تعيينكنندهاي از بلوكها محاسبه شده و سابقة مقادير هر يك از اين ويژگيها، به صورت تركيبي از توزيعهاي گوسين مدل ميگردد. با ورود هر فريم جديد، اين توزيعها با روش سريعي بهنگام ميشوند آنگاه توزيعهاي گوسين مدلهاي تركيبي، براي يافتن توزيعهايي كه بيانكنندة زمينه هستند ارزيابي ميشوند و هر بلوك بر اساس اينكه مقادير ويژگيهاي آن جزو كداميك از توزيعها باشد در دو كلاس زمينه و روزمينه دستهبندي ميشود. پيادهسازي نرمافزاري اين روش روي كامپيوتر شخصي، حاكي از عملكرد قابل قبول سيستم در برابر اجسام متجاوز به صحنه (مستقل از سرعت حركت آنها)، اضافه يا كاسته شدن اجسام داخل صحنه، نويز تصويربرداري و تغييرات ناخواستة صحنه است و سرعت بالا و نياز به حافظة كم، آنرا براي درصد بالايي از كاربردهاي بلادرنگ مناسب ميسازد.
The segmentation of foreground regions in image sequences is the first and the most important stage in many automated visual surveillance applications; and background subtraction is a method typically used for such applications. In this method, each new frame is compared with a model of the empty scene (which we call it ‘Background’), then those regions in the image that differ significantly from the background are identified as foreground. This paper presents a new background subtraction approach. In this method, each image is divided into similar NN blocks; then, some features are extracted from every block and the history of each feature are modeled as a combination of gaussian distributions. These distributions are updated after reception of every frame information. Then the gaussian distributions of the adaptive mixture models are evaluated to determine which one most likely describes the background and each block is classified as background or foreground based on the gaussians distributions which represents its feature value most effectively. The software implementations on personal computers show accepting capability of this approach for handling intruders to the scene, objects being introduced or removed from the scene, noises and unwanted changes in the background. Also, high speed of execution and reduced memory requirements makes this approach as a suitable method for high percentage of real-time applications
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