I am a fourth year PhD student at Center for Research in Computer Vision (CRCV) at UCF, advised by Dr. Boqing Gong. I received my Bachelor's degree in Electrical Engineering from Sharif University of Technology, Tehran, Iran.
My current research focus and interest is in intersection of Machine Learning and Computer Vision.
Authors: Aidean Sharghi, Jacob Scott Laurel, Boqing Gong
In recent years, there has been a resurgence of interest in video summarization. However, one of the main obstacles for the research on video summarization is the subjectiveness — users have diverse 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 and automatic 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.
Video data is explosively growing. As a result of the “big video data”, intelligent algorithms for automatic video summarization have (re-)emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot’s relevance to the user query and importance in the context of the video, jointly. We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos.
Authors: Thomas Castelli, Aidean Sharghi, Don Harper, Alain Tremeau, Mubarak Shah
In recent years, consumer Unmanned Aerial Vehicles have become very popular, everyone can buy and fly a drone without previous experience, which raises concern in regards to regulations and public safety. In this paper, we present a novel approach towards enabling safe operation of such vehicles in urban areas. Our method uses geodetically accurate dataset images with Geographical Information System (GIS) data of road networks and buildings provided by Google Maps, to compute a weighted A* shortest path from start to end locations of a mission. Weights represent the potential risk of injuries for individuals in all categories of land-use, i.e. flying over buildings is considered safer than above roads. We enable safe UAV operation in regards to 1- land-use by computing a static global path dependent on environmental structures, and 2- avoiding flying over moving objects such as cars and pedestrians by dynamically optimizing the path locally during the flight. As all input sources are first geo-registered, pixels and GPS coordinates are equivalent, it therefore allows us to generate an automated and user-friendly mission with GPS waypoints readable by consumer drones’ autopilots. We simulated 54 missions and show significant improvement in maximizing UAV’s standoff distance to moving objects with a quantified safety parameter over 40 times better than the naive straight line navigation.