An improved spatiogram similarity measure for robust object localisation

Spatiograms were introduced as a generalisation of the commonly used histogram, providing the flexibility of adding spatial context information to the feature distribution information of a histogram. The originally proposed spatiogram comparison measure has significant disadvantages that we detail here. We propose an improved measure based on deriving the Bhattacharyya coefficient for an infinite number of spatial-feature bins. Its advantages over the previous measure and over histogram-based matching are demonstrated in object tracking scenarios

Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

Gesture Recognition Based on Computer Vision on a Standalone System

Our project uses computer vision methods gesture recognition in which a camera interfaced to a system captures real time images and after further processing able to recognize the gesture shown to be interpreted. Our project mainly aims at hand gestures and after extracting information we try to produce it as an audio or in some visual form. We have used adaptive background subtraction with Haar classifiers to implement segmentation then we used convex hull and convex defects along with other feature extraction algorithms to interpret the gesture. First, this is implemented on a PC or laptop and then to produce a standalone system, we have to perform all this steps on a system which is dedicated to perform only the given specified task. For this we have chosen Beaglebone Black as a platform to implement our idea. The development comes with ARM Cortex A8 processor supported by NEON processor for video and image processing. It works on a clock frequency of maximum 1 GHz. It is 32 bit processor but it can be used in thumb mode i.e. it can work in 16 bit mode. This board supports Ubuntu, Android with some modification. Our first task is to interface a camera to the board so that it can capture images and store those as matrices followed by our steps to modify the installed Operating System to our purpose and implement all the above processes so that we can come up with a system which can perform gesture recognition

PENERAPAN OBJECT TRACKING DENGAN METODE ADAPTIVE PARTICLE FILTER UNTUK PELACAKAN BOLA PADA PERMAINAN

Data pergerakan bola dapat dimanfaatkan sebagai panduan untuk mengamati kejadian-kejadian pada pertandingan tenis yang telah berlangsung. Namun, untuk mendapatkan data pergerakan bola dari video pertandingan rentan terjadi kesalahan dalam pendeteksian objek, sehingga data yang dihasilkan terdapat noise. Berdasarkan alasan tesebut, penulis melakukan mining terhadap video pertandingan bola tenis dengan pendekatan object tracking, sehingga kesalahan deteksi ketika mendeteksi bola dapat dikurangi. Pendekatan tersebut diwujudkan dengan merancang model pelacakan bola dengan metode circle hough transform untuk mendeteksi lingkaran, kemudian dilanjutkan dengan metode pelacakan adaptive particle filter yang berfungsi untuk menghilangkan noise yang dihasilkan ketika melakukan deteksi lingkaran. Model tersebut dijalankan melalui proses-proses yang diantaranya adalah segmentasi citra, deteksi lingkaran, pelacakan objek dan diakhiri dengan koreksi lintasan. Model yang dirancang kemudian diimplementasikan pada bahasa pemrograman Phyton dan library OpenCV. Tahap terakhir dalam penelitian ini adalah melakukan eksperimen, eksperimen ini bertujuan untuk mendapatkan parameter masukan terbaik pada perangkat lunak, sehingga dapat diketahui efektifitas dari model yang telah diimplementasikan. Hasil eksperimen menunjukan bahwa video dengan jenis siaran pada lapangan hard court outdoor menghasilkan keluaran terbaik dengan rata-rata error sebesar 0,344, sedangkan hasil pengujian pada parameter lainnya harus disesuaikan dengan jenis video masukan agar mendapat error minimal.----------Ball movement data can be utilized as a guide for observing the events on the tennis matches that has lasted. However, the movement of the ball to get the data from the video game of the vulnerable object detection in error, so that the resulting data there is noise. Based on the reasons are, the author does mining against video game tennis ball with object tracking approach, so the error detection when it detects the ball can be reduced. The approach embodied by designing a model tracking ball with hough transform for circle method to detect circles, then proceed with adaptive particle filter tracking method that serves to eliminate noise generated when the detection loop. The model is run through processes such as image segmentation, object tracking, circle detection and end with correction trajectory. Model designed then implemented in the programming language Python and OpenCV library. The last stage in this research is doing experiments, this experiment aims to get the best input parameters in the software, so it can be known to the effectiveness of the model that has been implemented. Experimental results show that the type of video broadcast on an outdoor hard court field produce the best output with an average error of 0.344, whereas the test results on the other parameters must be adjusted to the type of video input so that it gets the error minimal

Object Tracking in Video using Mean Shift Algorithm including Effect of AWGN channel

One of the analytic ventures in object tracking is the tracking of fast-moving objects in arbitrary movement, mainly in the area of video vision applications. Thus a technique of mean shift (MS) algorithm in visual video tracking is put forward. In this suggested method, arbitrary motion and partial occlusion of an object can be managed due to its capacity in estimating the object position with modifying motion model. Although the techniques like particle filter (PF) is able to manage numerous hypotheses to manipulate clutters in background and short-term breakdown. However, on the other hand, it needs a huge number of particles to estimate the actual posterior of the target dynamics. Therefore, MS algorithm is employed to the sampling process of the PF to carry these particles in gradient ascent direction. As a result of this, a little sample size will be adequate to constitute the system dynamics precisely. The proposed algorithm is directed to track the moving object in arbitrary directions under altering states with reasonable computational time. The dissimilarity between the target model and the target candidates is expressed by a metric derived from the Bhattacharya Coefficient