Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

Abstract

In this paper, we present a fast and efficient technique for the data association problem applied to visual tracking systems. Visual tracking process is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc.We introduce a Tabu Search algorithm which performs a search on an indirect space. A novel problem formulation allows us to transform any solution into the real search space, which is needed for fitness calculation, in linear time. This new formulation and the use of auxiliary structures yields a fast transformation from a blob-to-track assignment space to the real shape and position of tracks space (while calculating fitness in an incremental fashion), which is key in order to produce efficient and fast results. Other previous approaches are based on statistical techniques or on evolutionary algorithms. These techniques are quite efficient and robust although they cannot converge as fast as our approach.

Systematic Review and Classification on Video Surveillance Systems

“…Video processing techniques (like model or feature driven approach, spatial differentiat ion, background removal, background frame differencing, optical flow field, feature tracking, edge detection, etc) not only offers segmentation of video into foreground and background elements to obtain the desired object of interest but also, it is a vital preprocess for applications such as human motion analysis and object based video encoding. [71], [72], [73], [74] [75], [76], [77] [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88] [89], [90], [4] …”

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

“…Faceimagesconveyvariousinformationabouttheemotionalstate,identity,gender,ethnicorigin, head orientation, and age of the person. This information is very important during face-to-face communicationamonghumans.Thefacialinformationduringinteractionismadepossiblebyhumans toidentifyandinterpretfacialgesturesandfacesaccuratelyinarealtimemanner (Lanitis,et al, 2004).Here,thehumanagesarepredictedfromtherelatedfacialimages (ThomasandRangachar, 2019)isverychallenginginthecomputervision.Thepopularityofthistopicisduetotheirpotential applications,likeeffectivefilteringincriminalinvestigationhuman-computerinteraction,intelligent advertising,andsoon (Dotu,et al,2011) (Hakeem,et al,2012) (XieandPun,2015).Themajor challengeisowedpartiallytouncertaintyinthefaceagingprocessesthatmanifestasthepersonalized conditionsareunderthesameage.Inthiscase,theuncertaintyiscausedbyextrinsicandintrinsic factors,respectively (XieandPun,2015).…”