BFO vs. BSO for video object tracking using particle filter (PF)

In this paper, we introduced a new algorithm for Video tracking, which is the process of locating a moving object (or multiple objects) over time using a camera. A new particle filter based on bacteria foraging optimization (PF-BFO) is introduced in field of video object tracking. This paper reviews particle filter and using it for tracking. Particle Swarm Optimization (PSO) is also described. Moreover, using the combination of PSO with PF (PF-PSO) in video object tracking is reviewed. Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from forging behavior of E. coli. After analysis of optimization mechanism, a series of measures are taken to improve the classic BFO by using Particle filter. The PSO is a meta-heuristic which is also inspired from insects' life as ACO. Even both methods use a population of entities. The comparison between PF-BFO and PF-PSO for video object tracking is presented in this work. The results show that PF is strong tool in tracking field. On the other hand, PF-BFO method presents outstanding performance versus PF-PSO

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

SIMULASI DAN ANALISIS MULTIPLE OBJECT TRACKING BERBASIS CITRA DENGAN METODE HIERARCHICAL PARTICLE FILTER

ABSTRAKSI: Objek tracking merupakan suatu bidang pada computer vision yang mempelajari cara melacak suatu objek yang bergerak pada suatu ruang. Objek yang dilacak merupakan objek yang sudah ditentukan. Pelacakan suatu objek bergerak sangat berguna untuk membantu tugas penting dalam aplikasi komputer vision seperti: pengenalan gerakan, pelacakan kendaraan, penghitungan jumlah kendaraan, augmented reality dan video kompresi. Object tracking mempunyai beberapa masalah antara lain adanya noise, kekacauan oklusi, dan perubahan dinamis dalam gerakan objek. Sehingga pada tugas akhir ini, dirancang sebuah sistem multiple object tracking dengan metode particle filter. Particle filter, juga dikenal sebagai sequential Monte Carlo merupakan salah satu metode stokastik yang telah dikembangkan dalam suatu komunitas visi komputer dan diterapkan untuk masalah pelacakan.Sistem pelacakaan ini bekerja dengan masukan secara non-real time. Objek yang dilacak berupa manusia. Proses pendeteksian manusia menggunakan metode Histogram of Oriented Gradient. Setelah melakukan deteksi manusia, maka objek tersebut akan dilacak dengan metode particle filter. Proses pelacakan dilakukan dengan cara membangkitkan random partikel pada area dekat dengan objek. Selanjutnya dilakukan proses model observasi untuk menghitung kemungkinan dari partikel tersebut yang mempunyai kesamaan histogram dengan objek target. Penghitungan kesamaan dilakukan dengan menggunakan Bhattacharyya coefficient.Pengujian pada sistem ini dengan menggunakan parameter yang diubah-ubah yaitu threshold maksimum, threshold minimum, jumlah partikel, jumlah objek dan kondisi perekaman objek. Tingkat akurasi terbaik pada threshold 0.88 dan threshold minimum 0.73. Jumlah objek pada proses pelacakan menggunakan particle filter berbanding lurus dengan waktu pemrosesan, sedangkan pada pelacakan dengan metode Histogram of Oriented Gradient berbanding lurus dengan resolusi video.Kata Kunci : object tracking, histogram, Bhattacharyya coefficient, Histogram of Oriented Gradient, particle filter.ABSTRACT: Object tracking is a field in computer vision that learn how to track a moving object in a space. Tracked object is an object that has been determined. Tracking a moving object is very useful to help an important task in computer vision applications such as: the introduction of motion, vehicle tracking, vehicle counts, augmented reality and video compression. Object tracking has many problems such as the noise, clutter occlusion, and dynamic changes in the motion of the object. So that the final project, designed a multiple object tracking system with particle filter method. Particle filters, also known as sequential Monte Carlo is a stochastic method that has been developed in the computer vision community and applied to tracking problems.Tracking’s system works by using video processing in a non-real time. Tracked object in the form of humans. In the human detection process, use the Histogram of Oriented Gradient method. After the detection of human doing, then the object will be tracked with a particle filter method. Tracking process is done by generating random particles at close to the object area. Then performed the observations made models to calculate the probability of a particle that has the same histogram as the target object. Similarity calculation is done by using the Bhattacharyya coefficient. Then made a new estimate of the object\u27s position.Testing the system using modified parameters namely maximum threshold, the minimum threshold, the number of particles, the number of objects and the recording condition of the object. Best accuracy rate at 0.88 threshold and the minimum threshold of 0.73. Total objects in the tracking process using the particle filter is directly proportional to the processing time, whereas in the tracking Histogram of Oriented Gradient method proportional to the resolution of the video.Keyword: object tracking, histogram, Bhattacharyya coefficient, Histogram of Oriented Gradient, particle filters

Multiple object tracking using an automatic veriable-dimension particle filter

Object tracking through particle filtering has been widely addressed in recent years. However, most works assume a constant number of objects or utilize an external detector that monitors the entry or exit of objects in the scene. In this work, a novel tracking method based on particle filtering that is able to automatically track a variable number of objects is presented. As opposed to classical prior data assignment approaches, adaptation of tracks to the measurements is managed globally. Additionally, the designed particle filter is able to generate hypotheses on the presence of new objects in the scene, and to confirm or dismiss them by gradually adapting to the global observation. The method is especially suited for environments where traditional object detectors render noisy measurements and frequent artifacts, such as that given by a camera mounted on a vehicle, where it is proven to yield excellent results

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application