Multi-object tracking is a well known problem in the context of vehicle tracking. Kalman filter is a common tool to solve the problem in real world. In a driving enviroment, there are other parameters affecting the behavior of the driver than itself such as other driver’s behavior and the environment including obstacles and possible paths. Interactive Kalman filter (IKF), a generalized from of DKF, was previously introduced to model the interaction between vehicles. To augment KF, DKF, and IKF, we use information extracted from history of traffic in the same environment called situation assessment. In this paper, we proposed SAIKF, a variant of Kalman filter and interactive Kalman filter that employs situation assessment information to enhance the performance of tracking. A graph called Motion History Graph is constructed based on the history of the vehicle motions and is then used to augment the estimation. The results on real world video sequences show effective performance improvement.

In this paper, an interactive Kalman filter (IKF) is proposed to demonstrate the interaction between targets as to how the behavior of a desired target is affected by the behavior of its neighbors. The IKF utilizes two types of interactions available in multi-agent systems, namely, cooperative and competitive. The IKF is similar to the distributed Kalman filter (DKF) in terms of architecture, method of representation of equations, and use of neighborhood weight matrix while IKF appears to be a general form of DKF. In this method, a network of IKF nodes is constructed such that each node is associated with every target. There are edges between nodes for which the corresponding targets have effect on each other. Time-varying weights are used to control the interaction information exchanged among IKF nodes. The method of calculating interaction weights in the weight matrix plays a key role on the estimation results. The calculation of optimal IKF gain and evaluations on MOTP, MOTA, and MSE metrics illustrate the effectiveness of the proposed filter in vehicle tracking.

Geometrical wavelets have already proved their strength in approximation, compression, and denoising of piecewise constant and piecewise linear images. In this paper, we extend this family by introducing planelets toward an effective representation of indoor depth images. It uses a linear fractional model to capture non-linearity of depth values in the planar regions of the output images of Kinect-like sensors. A block-based compression framework based on planelet approximation is then presented, which uses quadtree decomposition along with spatial predictions as an effective intra-coding scheme. Compared with both classical geometric wavelets and some state-of-the-art image coding algorithms, our method provides desirable quality by explicitly representing edges and planar patches.

In this paper, a new classifier fusion method is introduced based on a decision template structure as an extension to Dempster Shafer method. It employs multilayer neural networks as base classifiers. The idea relies on the fact that in a multilayer neural network, behavior of each layer can be a guide for modeling decision-making process. The new decision template based method constructs decision template for each layer of the neural networks including all hidden layers such that a complete model of the base classifiers decision making process is built. In the combiner part, a new strategy based on extension to Dempster Shafer method is introduced. Efficiency of this method is compared with some known benchmark datasets.

In the real world, we commonly receive information simultaneously through two or more senses, with the brain fusing this data to produce a single coherent message. Lip-reading is one example of this phenomenon. Laboratory studies, on the other hand, often measure the response to a stimulus by a single sense and extrapolate these results to predict real-world behavior. In this paper, we show that semantics have a significant impact on viewers' sensitivity to the quality of a video sequence for spatially separated parts of the sequence and, more importantly, that this difference in sensitivity can be changed by the presence of an audio signal. This result is important for any testing of subjects' responses to visual material. One example is the subjective assessment of the quality of video in an audio-visual communications system (such as television or videoconferencing).

In the real world, we commonly receive information simultaneously through two or more senses, with the brain fusing this data to produce a single-coherent message. Lip-reading is one example of this phenomenon. Laboratory studies, on the other hand often measure the response to a stimulus by a single sense and extrapolate these results to predict real-world behaviour. In this paper, we show that semantics have a significant impact on viewers' sensitivity to the quality of a video sequence for spatially separated parts of the sequence-and, more importantly, that this difference in sensitivity can be changed by the presence of an audio signal. This result is important for any testing of subjects' responses to visual material. One example is the subjective assessment of the quality of video-in an audio-visual communications system (such as television or video conferencing).