2,887 research outputs found
Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles
Abstract This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Recent years have witnessed a resurgence of interest in video summarization.
However, one of the main obstacles to the research on video summarization is
the user subjectivity - users have various preferences over the summaries. The
subjectiveness causes at least two problems. First, no single video summarizer
fits all users unless it interacts with and adapts to the individual users.
Second, it is very challenging to evaluate the performance of a video
summarizer.
To tackle the first problem, we explore the recently proposed query-focused
video summarization which introduces user preferences in the form of text
queries about the video into the summarization process. We propose a memory
network parameterized sequential determinantal point process in order to attend
the user query onto different video frames and shots. To address the second
challenge, we contend that a good evaluation metric for video summarization
should focus on the semantic information that humans can perceive rather than
the visual features or temporal overlaps. To this end, we collect dense
per-video-shot concept annotations, compile a new dataset, and suggest an
efficient evaluation method defined upon the concept annotations. We conduct
extensive experiments contrasting our video summarizer to existing ones and
present detailed analyses about the dataset and the new evaluation method
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