In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual input. However, the models obtained in other similar tasks, such as pedestrian collision risk analysis and human-robot interaction, are very difficult to generalize to the aerial filming task, because of the difference in data distributions. Towards improving generalization with less amount of labeled data, this paper presents a semi-supervised algorithm for heading direction estimation problem. We utilize temporal continuity as the unsupervised signal to regularize the model and achieve better generalization ability. This semi-supervised algorithm is applied to both training and testing phases, which increases the testing performance by a large margin. We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced. We also discuss several important details on improving the performance by balancing labeled and unlabeled loss, and making good combinations. Experimental results show that our approach robustly outputs the heading direction for different types of actor. The aesthetic value of the video is also improved in the aerial filming task.
Heading direction estimation is a widely studied problem, in particular focused on humans and cars. One option to tackle the problem is to use inertial and GPS sensors to estimate human's @cite_29 @cite_35 or a car's @cite_7 heading direction. In the context of aerial filming, the target actor generally does not carry extra sensors; thus our emphasis on vision-based solutions in this paper.
@cite_35 @cite_29 @cite_7
2519253192 2077405386
The paper proposes a novel approach for direction estimation of a moving pedestrian as perceived in a 2-D coordinate of field camera. The proposed direction estimation method is intended for pedestrian monitoring in traffic control systems. Apart from traffic control, direction of motion estimation is also very important in accident avoidance system for smart cars, assisted living systems, in occlusion prediction for seamless tracking in visual surveillance, and so on. The proposed video-based direction estimation method exploits the notion of perspective distortion as perceived in monocular vision of 2-D camera co-ordinate. The temporal pattern of change in dimension of pedestrian in a frame sequence is unique for each direction; hence, the dimensional change-based feature is used to estimate the direction of motion; eight discrete directions of motion are considered and the hidden Markov model is used for classification. The experiments are conducted over CASIA Dataset A , CASIA Dataset B , and over a self-acquired dataset: NITR Conscious Walk Dataset . The balanced accuracy of direction estimation for these experiments yields satisfactory results with accuracy indices as 94.58 , 90.87 , and 95.83 , respectively. The experiment also justifies with suitable test conditions about the characteristic features, such as robustness toward improper segmentation, partial occlusion, and changing orientation of head or body during walk of a pedestrian. The proposed method can be used as a standalone system or can be integrated with existing frame-based direction estimation methods for implementing a pedestrian monitoring system. An efficient approach for deriving accurate pose and heading values through multi-sensor fusion of data from several inexpensive sensors (such as multiple GPS (Global Positioning Systems), EC (electronic compass), rate gyro) is presented. The proposed multisensor fusion approach is composed of several sub-methods namely initial heading calculation, classification and weighing (CnW), extended Kalman filter (EKF) and then covariance intersection (CI) algorithms. The consecutive implementation of the sub-methods gives an accurate heading value with lesser RMSE (root mean square error) compared to the original GPS COG (course over ground) and EC. Several experimental tests were done to confirm the good performance of the proposed process.