Performing an 'Athletic Movement Assessment' for Sports Jump Using State of the Art Video Analysis and Heuristics Techniques Like Body Detection and Displacement Assessment

This paper proposes a some novel and state of the art technique for analyzing the Athletic Movement (Vertical Jump) and feats  by analyzing video frame by frame.  Most common method to analyze "Athletic Movement" such as Jump and feats accomplished in them are either an observations made by an human expert / coach, or they are the values captured by measurement devices in the suit or wearables attached to the body of an athlete. Where former requires an access to the human expert, the later requires the special kind of a hardware / sensor that has capability to extract the body movement statistics with respect to time and space. Both methods are pretty accurate but due to their overhead in terms of necessity / dependence on 3rd party system or person. Not to mention along with the cost such methods come up with, they are often inaccessible in situations where one's just home practicing or when an athlete is just trying out something in own backyard or Gym (personal zones). Our target was here to reduce those dependencies and create such heuristics and algorithms that can help an individual athlete to assess the feats like Jump, Run, and Leap, without using any 3rd party systems, and be able to approximate the feats and compare them with the existing ones using only the cellphone device in their pocket. This paper focused on Jump sport. The system processed video frame by frame and Applying Histogram Of Oriented Gradient Technique to find Human in Frame and then track human from  initial to last and we are capable now to calculate pixel distance covered by human in Jump. We used some values like human height to find physical distance covered, Frame Per Frame (FPS) of video, Markers on screen of mobile while recording videos.To validate the algorithm results, a number of experiments were performed and then compare with the actual vertical jump height and derive a statistical relation between the proposed methodology and the traditional techniques. Proposed technique can also be used for calculating different statistics of sport person

Closest Match Based Information Retrieval and Recommendation Engine using Signature-Trees and Fuzzy Relevance Sorting

This paper proposes a recommendation technique to avoid exhaustive search to be ran on the database with thousands of records, before coming to a conclusion or inference, where it can be said that recommended thing is matching up to a significant percentage of what was initially desired. Often such searches involve not just the simple full-match search based on indexes, but also the partial or nearby match searches where which percentage of match between entities is relevant enough for ultimate recommendation. Usually these problems are tackled by various methods like Fuzzy operations, Reg-Ex searches, Clustering, Similarity Analysis each having its own set of effectiveness as well as efficiency. Our goal here was to create a search and recommendation system which can perform fuzzy-search and fuzzy-similarity-analysis with near-match percentages in an effective, efficient as well as user-friendly manner on thousands of records/ files/ rows with 100s of attributes/ features/ columns. Inspired from Google's Image Searching Algorithm, that search on the basis of signatures based on feature-extraction from each image, we have created Match engine, that read schema of data or files, compiles encoded signature and store them as an index. That index is then converted into a tree (S-Tree), on the basis of relevance of each field/ column and data frequency observed. After compilation done, system can now search and recommendation of best matches in very efficient manner. For further optimization we use heuristics like dividing feature sets into hard-filters and soft-filters, former demands full match and later demands fuzzy match. On arriving even one best match, we can retrieve other matches without searching.Our technique though not that modern and actually inspired, but based on ensemble methods used to provide fast and efficient results. We have proved quicker than full scan searches. In future we plan to make signature comparison engine on variety of advanced data types of features like Geo-coordinates and synonyms. And storing compiled signatures trees into distributed database/grid, query will run concurrently to match the results, or signatures passing through machine learning techniques. Currently system used for recipe recommendation and in future this will be used in applications like dating system’s, film and music recommendation