Multi-view Vehicle Detection based on Part Model with Active Learning

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods

A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

A Tunable Algorithm to Update a Reference Image

A change detection scheme used to detect objects in a complex real-life scene must be able to deal with illumination changes, shadows, and structural variations of the environment. Several approaches are based on subtracting a reference image, representing the background, from the current input image. The most used methods estimate the background image by applying some low-pass filters on the input image sequence. Many of them require an accurate calibration phase and rely on a careful selection of critical parameters. An algorithm based on Kalman filtering is suggested here to dynamically estimate the background reference image. The approach extends former works and faces the severe problems of parameter tuning and modeling approximations. An experimental analysis on the behavior of the proposed algorithm in presence of different illumination changes is performed using noisy synthetic data. The results are used to address the choice of values for the filter parameters. The effectiveness and robustness of the algorithm are evaluated on several tests that were carried out on real-life sequence