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Ev ve Ofis taşıma sektöründe lider olmak.Teknolojiyi takip ederek bunu müşteri menuniyeti amacı için kullanmak.Sektörde marka olmak.
İstanbul evden eve nakliyat
Misyonumuz sayesinde edindiğimiz müşteri memnuniyeti ve güven ile müşterilerimizin bizi tavsiye etmelerini sağlamak.
Real-Time Pedestrian Tracking with Bacterial Foraging Optimization
We introduce swarm intelligence algorithms
for pedestrian tracking. In particular, we present a modified
Bacterial Foraging Optimization (BFO) algorithm and
show that it outperforms PSO in a number of important metrics
for pedestrian tracking. In our experiments, we show
that BFO’s search strategy is inherently more efficient than
PSO under a range of variables with regard to the number
of fitness evaluations which need to be performed when
tracking. We also compare the proposed BFO approach
with other commonly-used trackers and present experimental
results on the CAVIAR dataset as well as on the difficult
PETS2010 S2.L3 crowd video.
Zombie Survival Optimization: A Swarm Intelligence Algorithm Inspired By Zombie Foraging
Search optimization algorithms have the challenge
of balancing between exploration of the search space
(e.g., map locations, image pixels) and exploitation of
learned information (e.g., prior knowledge, regions of
high fitness). To address this challenge, we present a
very basic framework which we call Zombie Survival
Optimization (ZSO), a novel swarm intelligence ap-
proach modeled after the foraging behavior of zombies.
Zombies (exploration agents) search in a space where
the underlying fitness is modeled as a hypothetical air-
borne antidote which cures a zombie’s aliments and
turns them back into humans (who attempt to survive by
exploiting the search space). Such an optimization al-
gorithm is useful for search, such as searching an image
for a pedestrian. Experiments on the CAVIAR dataset
suggest improved efficiency over Particle Swarm Op-
timization (PSO) and Bacterial Foraging Optimization
(BFO). A C++ implementation is available.
Tracking Pedestrians with Bacterial Foraging Optimization Swarms
Pedestrian tracking is an important problem with
many practical applications in fields such as security, animation,
and human computer interaction (HCI). We
introduce a previously-unexplored swarm intelligence approach
to multi-object monocular tracking by using Bacterial Foraging
Optimization (BFO) swarms to drive a novel part-based pedestrian
appearance tracker. We show that tracking a pedestrian
by segmenting the body into parts outperforms popular blobbased
methods and that using BFO can improve performance
over traditional Particle Swarm Optimization and Particle Filter
methods.
Continuous learning of a multi-layered network topology in a video camera network
A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. This paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.
Tracking Multiple Objects in Non-Stationary Video
One of the key problems in computer vision and pattern
recognition is tracking. Multiple objects, occlusion, and
tracking moving objects using a moving camera are some
of the challenges that one may face in developing an ef-
fective approach for tracking. While there are numerous
algorithms and approaches to the tracking problem with
their own shortcomings, a less-studied approach considers
swarm intelligence. Swarm intelligence algorithms are often
suited for optimization problems, but require advancements
for tracking objects in video. We present an improved algorithm based on Bacterial Foraging Optimization
in order to track multiple objects in real-time video exposed
to full and partial occlusion, using video from both ¯xed and
moving cameras. A comparison with various algorithms is
provided.
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