Real Time Multiple Object Tracking and Occlusion Reasoning Using Adaptive Kalman Filters

Azari, Mohammad Mahdi; Seyfi, Ahamd; Rezaie, Amir Hossein

doi:10.1109/iranianmvip.2011.6121614

Cited by 12 publications

(13 citation statements)

References 7 publications

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“…Some works first binarize the images and others use a matching template. Rojas et al (2012), Gárate Polar (2015) and Hidayatullah et al (2015) proposed a fixed threshold and then a binary morphological filter; Buchelly et al (2016) applied a mathematical morphology gray filter to highlight sperm heads and a later threshold; Liu et al (2013), Vinaykumar and Jatoth (2014) applied temporal frame differencing, a fixed threshold and a binary morphological filter and others use background subtraction, thresholding and binary filtering (Azari et al 2011, Jeong et al 2014. Other approaches also exist that use simultaneous detection and tracking with their own considerations (Karthikeyan et al 2012, Boryshpolets et al 2013.…”

Section: Discussionmentioning

confidence: 99%

“…In order to define an object's model, kinematic variables, shape or geometric descriptors, contours, gray levels or textures can be considered (Lucena López 2003, Azari et al 2011, Liu et al 2013, Jeong et al 2014, Sahbani & Adiprawita 2016. From this set of data, the model is represented by the state x i of the system at the instant i with a given number of degrees of freedom (Lucena López 2003 As it does not rely on the geometry of the cell head or on gray levels information, a spermatozoon was treated as a point particle.…”

Section: Sperm Cells Trackingmentioning

confidence: 99%

“…Despite its limitations, Kalman filter is ideal to use with Gaussian and unimodal distributions (Lucena López 2003), assuming constant or low acceleration rates (Vinaykumar & Jatoth 2014). We associated Kalman filter to each detected particle to predict its future position (Catlin 1989, Azari et al 2011, Jeong et al 2014, as follows: let the state of a single particle j at the instant i, and the measurement vector. The state and the measurement are estimated by using Equations (4) and 5:…”

Section: Sperm Cells Trackingmentioning

confidence: 99%

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Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Zalazar

Pastore²,

Greco

et al. 2018

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This work offers researchers the first version of an open-source sperm tracker software (Sperm Motility Tracker, V1.0) containing a novel suit of algorithms to analyze sperm motility using ram and buck sperm as models. The computer-assisted semen analysis is used in several publications with increasing trend worldwide in the last years, showing the importance of objective methodologies to evaluate semen quality. However, commercial systems are costly and versatility is constrained. In the proposed method, segmentation is applied and the tracking stage is performed by using individual Kalman filters and a simplified occlusion handling method. The tracking performance in terms of precision (number of true tracks), the percentage of fragmented paths and percentage of correctly detected particles were manually validated by three experts and compared with the performance of a commercial motility analyzer (Microptic's SCA). The precision obtained with our sperm motility tracker was higher than the one obtained with a commercial software at the current acquisition frame rate of 25 fps ( < 0.0001), concomitantly with a similar percentage of fragmentized tracks ( = 0.0709) at sperm concentrations ranging 25-37 × 10 cells/mL. Moreover, our tracker was able to detect trajectories that were unseen by SCA. Kinetic values obtained by using both methods were contrasted. The higher values found were explained based on the better performance of our sperm tracker to report speed parameters for very fast motile sperm. To standardize results, acquisition conditions are suggested. This open-source sperm tracker software has a good plasticity allowing researchers to upgrade according requirements and to apply the tool for sperm from a variety of species.

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Section: Discussionmentioning

confidence: 99%

Section: Sperm Cells Trackingmentioning

confidence: 99%

Section: Sperm Cells Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Zalazar

Pastore²,

Greco

et al. 2018

Reproduction

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show abstract

“…Kalman filter is used by many application to track the objects. [7] To increase the reliability of object tracking shortest path in graph is used to identify the correct object [12]. Dynamic programming technique is used by authors to compare the object identity in case of occlusion.…”

Section: Introductionmentioning

confidence: 99%

Object Tracking using Association Rule in Clutter Environment

Halkarnikar¹,

Talbar²,

Vasambekar³

2015

IJIRSET

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ABSTRACT:In multiple objects tracking it become difficult to track the objects when they get cluttered due to proximity of the object. Such a cluttered environment leads to misleading target tracking in video analysis. This becomes important when video system is employed for security purpose or behavior analysis of the object. The object get merged and split due to occlusion or obstacles in viewing angle of the camera. In this paper we present the novel algorithm to handle issue of split and merge of the objects. To increase the robustness, association rules for object tracking are proposed. The algorithm tracks number of objects by keeping record of the split and merge of these objects with each other. Association rules are developed to track the multiple objects from frame to frame. Due to association rule application processing time increases when objects are merged or split, otherwise time required is same as normal object detection condition. In order to save the memory requirement for association of objects from frame to frame, linked list structure is implemented, which will expand and collapse as number of objects changes in given video frame. Object descriptors are stored as one node of the linked list along with object ID and flags indicating split and merge of objects. This list is updated as the video frames progresses for tracking of the objects. Such a system shows good result while tracking the multiple objects in cluttered environment due to shadow or occlusion or overlapping with in a frame.

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“…Color cannot capture the characteristics of the target as it is not suitable for non rigid object. Edge or Shape is not reliable for non-rigid and rotating object [4] [20]. And the efficiency of the algorithm is limited by the higher dimensional feature representation.…”

Section: Introductionmentioning

confidence: 99%