آشكارسازي و رديابي اهداف متحرك در تصاوير پرنده‌هاي بدون سرنشين

Detecting and Tracking Moving Objects in Unmanned Aerial Vehicles (UAVs) Images

يكي از قابليت‌هاي ارزشمند پرنده‌هاي بدون سرنشين (پهپادها) توانايي آن‌ها براي رديابي اهداف مشخص و در حال حركت درروي زمين است. چالش مهم در رديابي اهداف متحرك با كمك پهپاد، حركت پس‌زمينه تصوير همراه با شي‌ء موردنظر است. در اين تحقيق الگوريتم جديدي براي آشكارسازي و رديابي اهداف متحرك با استفاده از هم مرجع سازي فريم به فريم تصوير اخذشده ارائه‌شده است به‌طوري ‌كه نياز به منبع ديگر جهت اطلاع از جهت حركت و وضعيت پهپاد ندارد. در اين روش، جهت بهبود تناظر يابي از الگوريتم SURF بهبوديافته كه اطلاعات رنگ را به توصيفگر ايجادشده توسط الگوريتم SURF متداول اضافه مي‌نمايد، استفاده مي‌شود. در مرحله ي بعد، براي از بين بردن تناظرهاي ضعيف الگوريتم RANSAC بكار گرفته مي‌شود. پس از پيدا كردن نقاط متناظر قابل‌اعتماد در تصاوير فريم‌هاي متوالي، پارامترهاي بين دو تصوير با استفاده از تبديل پروژكتيو محاسبه مي‌شود. در مرحله آخر براي آشكارسازي شي‌ء متحرك به ترتيب از روش‌هاي تفريق فريم‌ها و قطعه‌بندي تصوير استفاده مي‌شود. نتايج تجربي به‌دست‌آمده بر روي تصاوير ويدئويي اخذشده از يك پهپاد، نشان مي‌دهد كه روش پيشنهادي عملكرد مناسبي در آشكارسازي و رديابي اهداف، با سرعت پردازش متناسب ارائه مي‌دهد.

Unmanned Aerial Vehicles (UAVs) are promising tools for many applications, including agriculture, mining, recreation, search and rescue, infrastructure monitoring, and wildlife research and conservation. Many of these applications require some type of object tracking. In fact, one of the most valuable capabilities of UAVs is their ability to detect and track particular moving targets on the ground. The main problem in moving target tracking using UAVs is the background movement with the intended target. In this study, a new method for detecting and tracking moving targets using frame-to-frame registration is presented. The proposed method does not require other external sources to know about the position and orientation of the platform. Also, computer vision processes are applied to detect moving objects and the detections are passed to a tracking algorithm, which then generates continuous tracks of objects seen by the camera. This paper first reviews the current methods utilized in multiple target tracking in video with particular emphasis on airborne applications. It then presents a method for detecting moving objects in video obtained from a UAV. In this method, the improved Speeded-Up Robust Features (SURF) matching algorithm that adds color information to conventional SURF descriptor is used to enhance matching results. The SURF algorithm is widely applied in the field of target detection and tracking. But in this study, the advantages of SURF and color information are combined to achieve better results. Then, Random Sample Consensus (RANSAC) technique is used to remove weak matches. After finding reliable corresponding points in consecutive frame images, the projective transformation between two frames is calculated. Finally for detection the moving object, frame subtraction and image segmentation methods are used respectively. Generally, the proposed method can be summarized like this: the suggested matching method using the improved SURF matching algorithm. The proposed method is implemented on video data obtained from a UAV which has a HD camera with the frame rate of 60 frames per second (fps) and the resolution of 1080×1920 pixels. It is studied to detect moving vehicles and people and the derived outcomes are analyzed. Experimental results show that the proposed method has suitable performance for moving object detection in images with moving background. However, the results show that although the improved SURF matching algorithm imposes some computational complexities to the proposed method, it eventuates to positive effects in matching results. Average processing time of the proposed method is also studied. Results show that although the average processing time is increased about 6%, the matching accuracy increased by an average of 8.5%. However, due to the important role of the matching accuracy on the accuracy of final results obtained from proposed algorithm, using the improved SURF matching algorithm is an appropriate choice. Also, other deliberations show efficiency of the proposed method in detection and tracking moving objects. It is also suggested that in segmentation stage, the performance of other techniques like Fuzzy segmentation algorithm would be evaluated in comparison with Niblack technique. Also the gained results of the proposed algorithm would be evaluated on the data recorded in different environmental conditions like different lightening situation, excessive shaking of the camera and foggy and dusty weather.