SPSA Based Feature Relevance Estimation For Video Retrieval

With the availability of a huge amount of video data on various sources, efficient video retrieval tools are increasingly in demand. Video being a multi-modal data, the perceptions of ``relevance'' between the user provided query video (in case of Query-By-Example type of video search) and retrieved video clips are subjective in nature. We present an efficient video retrieval method that takes user's feedback on the relevance of retrieved videos and iteratively reformulates the input query feature vectors (QFV) for improved video retrieval. The QFV reformulation is done by a simple, but powerful feature weight optimization method based on Simultaneous Perturbation Stochastic Approximation (SPSA) technique. A video retrieval system with video indexing, searching and relevance feedback (RF) phases is built for demonstrating the performance of the proposed method. The query and database videos are indexed using the conventional video features like color, texture, etc. However, we use the comprehensive and novel methods of feature representations, and a spatio-temporal distance measure to retrieve the top M videos that are similar to the query. In feedback phase, the user activated iterative on the previously retrieved videos is used to reformulate the QFV weights (measure of importance) that reflect the user's preference, automatically. It is our observation that a few iterations of such feedback are generally sufficient for retrieving the desired video clips. The novel application of SPSA based RF for user-oriented feature weights optimization makes the proposed method to be distinct from the existing ones. The experimental results show that the proposed RF based video retrieval exhibit good performance.

A Reactive Inverse PN Algorithm for Collision Avoidance among Multiple Unmanned Aerial Vehicles

While performing a mission, multiple Unmanned Aerial Vehicles (UAVs) need to avoid each other to prevent collisions among them. In this paper, we design a collision avoidance algorithm to resolve the conflict among UAVs that are on a collision course while flying to heir respective destinations. The collision avoidance algorithm consist of each UAV that is on a collision course reactively executing a maneuver that will, as in `inverse' Proportional Navigation (PN), increase Line of Sight (LOS) rate between them, resulting in a `pulling out' of collision course. The algorithm is tested for high density traffic scenarios as well as for robustness in the presence of noise.

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.

A model based factorization approach for dense 3D recovery from monocular video

Feature track matrix factorization based methods have been attractive solutions to the Structure-front-motion (Sfnl) problem. Group motion of the feature points is analyzed to get the 3D information. It is well known that the factorization formulations give rise to rank deficient system of equations. Even when enough constraints exist, the extracted models are sparse due the unavailability of pixel level tracks. Pixel level tracking of 3D surfaces is a difficult problem, particularly when the surface has very little texture as in a human face. Only sparsely located feature points can be tracked and tracking error arc inevitable along rotating lose texture surfaces. However, the 3D models of an object class lie in a subspace of the set of all possible 3D models. We propose a novel solution to the Structure-from-motion problem which utilizes the high-resolution 3D obtained from range scanner to compute a basis for this desired subspace. Adding subspace constraints during factorization also facilitates removal of tracking noise which causes distortions outside the subspace. We demonstrate the effectiveness of our formulation by extracting dense 3D structure of a human face and comparing it with a well known Structure-front-motion algorithm due to Brand.

Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles

Much of the benefits of deploying unmanned aerial vehicles can be derived from autonomous missions. For such missions, however, sense-and-avoid capability (i.e., the ability to detect potential collisions and avoid them) is a critical requirement. Collision avoidance can be broadly classified into global and local path-planning algorithms, both of which need to be addressed in a successful mission. Whereas global path planning (which is mainly done offline) broadly lays out a path that reaches the goal point, local collision-avoidance algorithms, which are usually fast, reactive, and carried out online, ensure safety of the vehicle from unexpected and unforeseen obstacles/collisions. Even though many techniques for both global and local collision avoidance have been proposed in the recent literature, there is a great interest around the globe to solve this important problem comprehensively and efficiently and such techniques are still evolving. This paper presents a brief overview of a few promising and evolving ideas on collision avoidance for unmanned aerial vehicles, with a preferential bias toward local collision avoidance.

Memory Bandwidth and Power Reduction Using Lossy Reference Frame Compression in Video Encoding

Large external memory bandwidth requirement leads to increased system power dissipation and cost in video coding application. Majority of the external memory traffic in video encoder is due to reference data accesses. We describe a lossy reference frame compression technique that can be used in video coding with minimal impact on quality while significantly reducing power and bandwidth requirement. The low cost transformless compression technique uses lossy reference for motion estimation to reduce memory traffic, and lossless reference for motion compensation (MC) to avoid drift. Thus, it is compatible with all existing video standards. We calculate the quantization error bound and show that by storing quantization error separately, bandwidth overhead due to MC can be reduced significantly. The technique meets key requirements specific to the video encode application. 24-39% reduction in peak bandwidth and 23-31% reduction in total average power consumption are observed for IBBP sequences.

Video coding mode decision as a classification problem

In this paper, we show that it is possible to reduce the complexity of Intra MB coding in H.264/AVC based on a novel chance constrained classifier. Using the pairs of simple mean-variances values, our technique is able to reduce the complexity of Intra MB coding process with a negligible loss in PSNR. We present an alternate approach to address the classification problem which is equivalent to machine learning. Implementation results show that the proposed method reduces encoding time to about 20% of the reference implementation with average loss of 0.05 dB in PSNR.

Analog/RF Built-in-Self-Test Subsystem for a Mobile Broadcast Video Receiver in 65-nm CMOS

A built-in-self-test (BIST) subsystem embedded in a 65-nm mobile broadcast video receiver is described. The subsystem is designed to perform analog and RF measurements at multiple internal nodes of the receiver. It uses a distributed network of CMOS sensors and a low bandwidth, 12-bit A/D converter to perform the measurements with a serial bus interface enabling a digital transfer of measured data to automatic test equipment (ATE). A perturbation/correlation based BIST method is described, which makes pass/fail determination on parts, resulting in significant test time and cost reduction.

Fault-tolerant Video on Demand in RSerPool Architecture

With the advent of Internet, video over IP is gaining popularity. In such an environment, scalability and fault tolerance will be the key issues. Existing video on demand (VoD) service systems are usually neither scalable nor tolerant to server faults and hence fail to comply to multi-user, failure-prone networks such as the Internet. Current research areas concerning VoD often focus on increasing the throughput and reliability of single server, but rarely addresses the smooth provision of service during server as well as network failures. Reliable Server Pooling (RSerPool), being capable of providing high availability by using multiple redundant servers as single source point, can be a solution to overcome the above failures. During a possible server failure, the continuity of service is retained by another server. In order to achieve transparent failover, efficient state sharing is an important requirement. In this paper, we present an elegant, simple, efficient and scalable approach which has been developed to facilitate the transfer of state by the client itself, using extended cookie mechanism, which ensures that there is no noticeable change in disruption or the video quality.

Perceptually motivated Rated Controller for Video coding

Rate control regulates the instantaneous video bit -rate to maximize a picture quality metric while satisfying channel constraints. Typically, a quality metric such as Peak Signalto-Noise ratio (PSNR) or weighted signal -to-noise ratio(WSNR) is chosen out of convenience. However this metric is not always truly representative of perceptual video quality.Attempts to use perceptual metrics in rate control have been limited by the accuracy of the video quality metrics chosen.Recently, new and improved metrics of subjective quality such as the Video quality experts group's (VQEG) NTIA1 General Video Quality Model (VQM) have been proven to have strong correlation with subjective quality. Here, we apply the key principles of the NTIA -VQM model to rate control in order to maximize perceptual video quality. Our experiments demonstrate that applying NTIA -VQM motivated metrics to standard TMN8 rate control in an H.263 encoder results in perceivable quality improvements over a baseline TMN8 / MSE based implementation.

Non Identical Duplicate Video Detection using SIFT Method

Non-Identical Duplicate video detection is a challenging research problem. Non-Identical Duplicate video are a pair of videos that are not exactly identical but are almost similar.In this paper, we evaluate two methods - Keyframe -based and Tomography-based methods to determine the Non-Identical Duplicate videos. These two methods make use of the existing scale based shift invariant (SIFT) method to find the match between the key frames in first method, and the cross-sections through the temporal axis of the videos in second method.We provide extensive experimental results and the analysis of accuracy and efficiency of the above two methods on a data set of Non- Identical Duplicate video-pair.

A New Iterative FIR Filter for Image and Video Restoration

Image and video filtering is a key image-processing task in computer vision especially in noisy environment. In most of the cases the noise source is unknown and hence possess a major difficulty in the filtering operation. In this paper we present an error-correction based learning approach for iterative filtering. A new FIR filter is designed in which the filter coefficients are updated based on Widrow-Hoff rule. Unlike the standard filter the proposed filter has the ability to remove noise without the a priori knowledge of the noise. Experimental result shows that the proposed filter efficiently removes the noise and preserves the edges in the image. We demonstrate the capability of the proposed algorithm by testing it on standard images infected by Gaussian noise and on a real time video containing inherent noise. Experimental result shows that the proposed filter is better than some of the existing standard filters