Automatic Moving Object Segmentation for Freely Moving Cameras

Abstract

This paper proposes a new moving object segmentation algorithm for freely moving cameras which is very common for the outdoor surveillance system, the car build-in surveillance system, and the robot navigation system. A two-layer based affine transformation model optimization method is proposed for camera compensation purpose, where the outer layer iteration is used to filter the non-background feature points, and the inner layer iteration is used to estimate a refined affine model based on the RANSAC method. Then the feature points are classified into foreground and background according to the detected motion information. A geodesic based graph cut algorithm is then employed to extract the moving foreground based on the classified features. Unlike the existing global optimization or the long term feature point tracking based method, our algorithm only performs on two successive frames to segment the moving foreground, which makes it suitable for the online video processing applications. The experiment results demonstrate the effectiveness of our algorithm in both of the high accuracy and the fast speed.

Background Subtraction for Moving Cameras Based on Trajectory-controlled Segmentation and Label Inference

“…The comparison of average Precision and Recall metrics is shown in Table 1. Compared with the algorithms [3,6,13,19,20], the proposed algorithm achieves the highest Precision and significant Recall, which indicates less false foreground/background pixels and more accurate segmentation result. [13] 0.8191 0.8270 [3] 0.6957 0.8903 [6] 0.6135 0.8058 [19] 0.8122 0.8263 [20] 0.8050 0.8465 Fig.5 shows some representative results of our method (video sequences cars 1, cars 4 and marple 7 provided by the dataset).…”

“…5, although the contours of the foregound objects are slightly affected by blurred boundaries and errors of location of trajectory points, out method can generate satisfying segmentation results. The visual effects of foreground segmentation for [3] [6] [13] [19] [20] and the proposed algorithm are shown in Fig. 6.…”

“…6. Compared with algorithms [3,6,13,19,20], the proposed method generates better visual results of foreground segmentation. Moving objects obtained using the proposed method show more accurate contours and boundaries of the objects.…”

“…Zhang et al [13] proposed a video object segmentation method based on the watershed algorithm and clustering region trajectories. Jiman Kim [19] and Yanli Wan [20] proposed new types of moving object segmentation methods for moving cameras.…”

Inverted tracking algorithm for the field survey through artificial vision and robotics

“…Donde hemos destacado sistemas de tracking en ambientes outdoor para secuencias de imágenes que no varían demasiado en el tiempo [3], tracking de objetos mediante la utilización de robots [4][5], vehícu-los aéreos no tripulados (UAV) [6] o tracking mediante la utilización de plataformas en movimiento [6] [7], aplicados por ejemplo, como asistencia al manejo de automóviles [8]. Por último, se encuentran trabajos donde se realiza el tracking de objetos en movimiento con cámaras en movimiento [8] [9] [10] [11]. A la hora de detectar los objetos a sobre los que se realiza el tracking se encuentran trabajos con diferentes tipos de segmentación de la imagen de video, destacando aquellos realizados mediante color [12] [13] [14], donde algunos además incorporan texturas [15][16] o formas [13] [8], o mediante histogramas [17], o trabajan directamente sin segmentación [18].…”