Visual tracking with conditionally adaptive multiple template update scheme for intricate videos

Joy, Emmanuel; Peter, J. Dinesh

doi:10.1007/s00530-017-0540-2

Cited by 3 publications

(7 citation statements)

References 37 publications

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“…In recent years, video surveillance has seen a progressive development in various fields, such as driving assistance, human-computer interaction, augmented reality, and so on [1]. In the computer vision applications, numerous data is collected for detecting the moving objects in the video frame.…”

Section: Introductionmentioning

confidence: 99%

“…Object tracking is the process of determining the positions and other significant information of moving objects in image sequences. Object retrieval from videos undergoes two major processes, listed as (1) detecting and tracking the position of objects and (2) defining object descriptors for feature extraction and retrieving the objects [2]. In [8], contour based schemes are developed for tracking the position of the object.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers involved in literature can be categorized as video retrieval, tracking objects in the video, trajectory matching, object appearance matching and so on [7]. In [1], visual tracking scheme has been used for tracking the objects. Visual tracking scheme [1] finds the position of the target object in consecutive frames of the object and finds it trajectory or moving path.…”

Section: Introductionmentioning

confidence: 99%

“…In [1], visual tracking scheme has been used for tracking the objects. Visual tracking scheme [1] finds the position of the target object in consecutive frames of the object and finds it trajectory or moving path. One of the major challenges involved in visual tracking scheme is detecting the target object in the video prevailed with long-term occlusions.…”

Section: Introductionmentioning

confidence: 99%

“…One of the major challenges involved in visual tracking scheme is detecting the target object in the video prevailed with long-term occlusions. Further, during the video processing schemes, the video may lose quality due to the rotation, and geometric deformation [1]. Techniques, such as region-based [11], feature-based [12], model-based [13], and active contour-based techniques [14] achieved significant results in visual object tracking [1].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Support vector regression and extended nearest neighbor for video object retrieval

Ghuge

Ruikar²,

Prakash

2018

Evol. Intel.

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Video retrieval is one of the emerging areas in video capturing that gained various technical advances, increasing the availability of a huge mass of videos. For the text or the image query given, retrieving the relevant videos and the objects from the videos is not always an easy task. A hybrid model was developed in the previous work using the Nearest Search Algorithm (NSA) and exponential weighted moving average (EWMA), for the video object retrieval. In NSA + EWMA, the object trajectories are retrieved based on the query specific distance. This work extends the previous work by developing a novel path equalization scheme for equalizing the path length of the query and the tracked object. Initially, a hybrid model based on Support Vector Regression and NSA tracks the position of the object in the video. The proposed density measure scheme equalizes the path length of the query and the object. Then, the identified path length related to the query is given to extended nearest neighbor classifier for retrieving the video. From the simulation results, it is evident that the proposed video retrieval scheme achieved high values of 0.901, 0.860, 0.849, and 0.922 for precision, recall, F-measure, and multiple object tracking precision, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%