Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multi-object tracking (MOT) in airborne video is a challenging problem due to the uncertain airborne vehicle motion as ll as mounted camera vibrations. Most approaches addressing tracking in such type of scenario, use data association based on motion detection responses. Such approaches fail tracking objects with low speed or static ones. To alleviate the motion detection failures, in this paper  propose a multi-object tracking system based on combining motion-detection and Compressive Tracking detection responses.   In this work, as in [1], the multi-object tracking problem is solved by associating tracklets according to their confidence values. For reliable association beten tracklets and detections,  propose using Compressive Tracking (CT) as a mean to detect objects when motion-detection fails. By exploiting the compressive tracking, which allows discriminating the appearances of objects, tracklet association can be successfully achieved even when objects undertake stop-and-go motion as ll as when they are partially occluded. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.
['Ting Chen', 'Hichem Sahli', 'Yanning Zhang', 'Tao Yang']
I am writing a research paper on a idea Multi-Object Tracking in Airborne Video Imagery based on Compressive Tracking Detection Responses
[{"from": "human", "value": "I am writing a research paper on a idea Multi-Object Tracking in Airborne Video Imagery based on Compressive Tracking Detection Responses"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multi-object tracking (MOT) in airborne video is a challenging problem due to the uncertain airborne vehicle motion as ll as mounted camera vibrations. Most approaches addressing tracking in such type of scenario, use data association based on motion detection responses. Such approaches fail tracking objects with low speed or static ones. To alleviate the motion detection failures, in this paper  propose a multi-object tracking system based on combining motion-detection and Compressive Tracking detection responses.   In this work, as in [1], the multi-object tracking problem is solved by associating tracklets according to their confidence values. For reliable association beten tracklets and detections,  propose using Compressive Tracking (CT) as a mean to detect objects when motion-detection fails. By exploiting the compressive tracking, which allows discriminating the appearances of objects, tracklet association can be successfully achieved even when objects undertake stop-and-go motion as ll as when they are partially occluded. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods."}]