Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions

Persepsi pelajar sarjana muda kejuruteraan elektrik terhadap program latihan industri, Kolej Universiti Teknologi Tun Hussein Onn

Kajian ini dijalankan bertujuan untuk mengetahui persepsi Pelajar Sarjana Muda Kejuruteraan Elektrik Terhadap Program Latihan Industri, KUiTTHO berdasarkan kepada 4 faktor iaitu kesesuaian penempatan program latihan industri, kesesuaian pendedahan pelajaran teori di KUiTTHO dan amali di tempat program latihan industri, tahap kerjasama yang diberikan oleh pihak industri kepada pelajar d a n kesediaan pelajar melakukan kerja yang diberi semasa program latihan industri. Sampel kajian adalah terdiri daripada pelajar-pelajar Sarjana Mud a Kejuruteraan Elektrik di KUITTHO yang telah menjalani program latihan industri. Set soal selidik terdiri daripada 3 bahagian iaitu bahagian A yang bertujuan untuk mendapatkan maklumat diri responden manakala bahagian Bertujuan untuk mengetahui kesesuaian program latihan industri yang telah diikuti oleh pelajar dan bahagian C adalah cadangan untuk meningkatkan mutu program latihan industri. Data - data yang diperolehi dianalisis menggunakan perisisan SPSS 10.0 for Windows (Statistical Package for the Social Science version 10) dan dipersembahkan dalam bentuk peratusan, carta dan keterangan analisis. Dapatan kajian secara umumnya menunjukkan reaksi positif dimana bagi semua aspek menunjukkan min keseluruhan yang tingg

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework