Self scale estimation of the tracking window merged with adaptive particle filter tracker

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker

SIMULASI DAN ANALISIS MULTIPLE OBJECT TRACKING MENGGUNAKAN METODE KERNEL PARTICLE FILTER

ABSTRAKSI: Penggunaan suatu kamera untuk sistem pengawasan merupakan teknologi yang berperan penting yang dapat mendukung sistem keamanan dan pengawasan suatu tempat dari jarak jauh. Digunakannya kamera ini penting untuk bukti tindak kejahatan dan pelanggaran. Namun, penggunaan kamera ini terkadang menjadi kurang efektif jika hanya digunakan untuk merekam saja tanpa adanya tracking/marking yang bisa mengikuti pergerakan dari tiap objek yang direkam. Object tracking diaplikasikan dan dikembangkan pada kamera perekam untuk melakukan tracking pada objek sehingga dapat diikuti pergerakan dari objek tersebut. Objek tracking ini sendiri adalah proses mengikuti posisi dari suatu objek yang diinginkan. Dalam tugas a khir ini dibangun sebuah simulasi atau software yang fungsinya sama dengan kamera CCTV . Simulasi ini akan menggunakan metode Kernel Particle Filter (KPF). Dengan menggunakan camcorder dilakukan pengambilan citra sehingga akan didapatkan gambar objek. Kemud ian gambar objek diproses menggunakan metode KPF tersebut untuk mengidentifikasi dan melakukan tracking gambar objek tersebut. Setelah objek dapat di tracking kemudian selanjutnya adalah membandingkan dengan data aktual. Hasil yang diperoleh dari Tugas Ak hir ini adalah sebuah sistem yang mampu melakukan tracking kendaraan berdasarkan jumlah frame input . Setelah dilakukan pengujian terhadap sistem, dapat diambil kesimpulan bahwa parameter terbaik untuk mendeteksi mobil yaitu parameter threshold absolut seli sih 10, parameter filter median 3, parameter jenis struktur elemen line90°, parameter ukuran struktur elemen 3, parameter filter bwareaopen 50. Akurasi rata - rata total dari 16 video uji terhadap intensitas cahaya pagi, siang, sore, dan malam yaitu 74.8125% . Nilai rata - rata jarak centroid hasil kernel particle filter lebih kecil dari hasil rata - rata jarak centroid dari hasil deteksi.Kata Kunci : multiple object tracking , kernel particle filter , computer vision , pelacakanABSTRACT: Camera usage for security system is a technology which has an important role that can support security and monitoring system from a remote place. Actual usage of a camera is also important as criminal evidence. However, the usage of camera is sometimes ineffective and inefficient if we use it just to record without tracking/marking that can follow the object’s movements.The object tracking is applied and developed in the recording camera for tracking object in order to follow the object’s movements. Object tracking i s a process to follow a desired object’s position. This thesis develops a simulation or software that has the same function with CCTV. This simulation is using kernel particle filter (KPF) method. By using camcorder, an image is captured and the object i s obtained. Then, the object will be processed by using KPF method to identify and track the object. After the object is tracked, then the next step is comparing the result with the actual data. The result of this thesis is a system that is able to track some cars from the sum of frame inputs. After system testing, it can be concluded that the best parameters to detect the car are the threshold absolute parameter is 10, median filter parameter is 3, the type of element structure is line90°, elements struc ture size is 3, and the bwareaopen filter is 50. The average accuracy rate from 16 videos regarding to morning, afternoon, evening, and night light intensity is 74.8125% . The average rate of centroid distance of kernel particle filter is smaller than the d etection result.Keyword: multiple object tracking , kernel particle filter , computer vision , trackin

Single and multiple target tracking via hybrid mean shift/particle filter algorithms

This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

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

Combining wireless and visual tracking for an indoor environment

There has been a lot of research done towards both camera and Wi-Fi tracking respectively, both these techniques have their benefits and drawbacks. By combining these technologies it is possible to eliminate their respective weaknesses, to increase the possibilities of the system as a whole. This is accomplished by fusing the sensor data from Wi-Fi and camera before inserting it in a particle filter. This will result in a more accurate and robust localization system

GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure