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Cited by 16 publications
(10 citation statements)
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References 39 publications
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“…The optimal approximation of Q is obtained by minimizing Kullback-Leibler (KL) divergence between P and Q. The solution for Q has the following form, log(Q i (d i |r, I)) = E i =j [log(P (d|r, I))] + const, (8) where E i =j denotes expectation under Q distributions over all variables d j for j = i. The inference is formulated as…”
Section: A Deep Continuous Conditional Random Field (Dccrf)mentioning
confidence: 99%
“…The optimal approximation of Q is obtained by minimizing Kullback-Leibler (KL) divergence between P and Q. The solution for Q has the following form, log(Q i (d i |r, I)) = E i =j [log(P (d|r, I))] + const, (8) where E i =j denotes expectation under Q distributions over all variables d j for j = i. The inference is formulated as…”
Section: A Deep Continuous Conditional Random Field (Dccrf)mentioning
confidence: 99%
“…The basic aim of multi-target tracking across non-overlapping cameras is to automatically recover the trajectories of all targets and keep their identities consistent while they travel from one camera to another camera [3]. Here we propose two methods for tracked target detection…”
Section: Proposed Workmentioning
confidence: 99%
“…The idea of multi-target tracking which is done by comparing both outputs that are output obtained from different cameras is compared with multi-cam dataset output. Using techniques for human detection, multi-target tracking system can play important role to capture location of people at public areas such as stores and travel sites and then produce congestion analysis to assist in the management of the people [3]. In such a way tracking system can monitor express ways and junctions of the road network.…”
Section: Introductionmentioning
confidence: 99%
“…MOT deals with the process of accurately estimating the state of objects -primarily, position, identity, and configuration -over time from a set of observations. Due to incurred challenges, e.g., scene clutter, target dynamics, intra/inter-class variation, measurement noise, sensor motion, and frame rate, it has long been established that coupling trackers with detectors, in a paradigm called trackingby-detection, helps to better tackle these challenges [4], [1], [5]. In our context, tracking-by-detection approaches rely on a people detector to start, update, re-initialize, guide (avoid drift), or terminate a tracker.…”
Section: Introductionmentioning
confidence: 99%