An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three detectors for detecting abnormal activity are generated using negative selection. Tracks gathered by people’s movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

A method for tracking rats in a complex and completely dark environment using computerized video analysis

The automated tracking of rodents in open field environments has become a standard laboratory technique for the investigation of the effects of drugs, novel therapeutic interventions and genetic mutations on behavior. Here, we develop an extension of this technique that permits tracking in full darkness through a complex (‘enriched’) environment comprising naturalistic structures such as tunnels and hides. To eliminate unwanted light reflections and tape noise, we developed a unique video filter that combines the advantages of differential and non-differential filtering. This filter enabled the tracking of albino rats against a relatively dark background to an accuracy of approximately 97% compared to hand tracking of the same animal, irrespective of whether the rat was inside a hide box or tunnel or out in the open field. The system as a whole can be easily deployed using standard PCs and inexpensive infrared cameras and lights.

Improved fade and dissolve detection for reliable video segmentation

We present improved algorithms for automatic fade and dissolve detection in digital video analysis. We devise new two-step algorithms for fade and dissolve detection and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from early work in scene change detection which focuses on identifying the existence of a transition rather than its precise temporal extent. We evaluate our algorithms against two other commonly used methods on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

New enhancements to cut, fade, and dissolve detection processes in video segmentation

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

Automatic genre identification for content-based video categorization

This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.

Detection of setting and subject information in documentary video

Interpretation of video information is a difficult task for computer vision and machine intelligence. In this paper we examine the utility of a non-image based source of information about video contents, namely the shot list, and study its use in aiding image interpretation. We show how the shot list may be analysed to produce a simple summary of the 'who and where' of a documentary or interview video. In order to detect the subject of a video we use the notion of a 'shot syntax' of a particular genre to isolate actual interview sections.

Adaptive depletion for improvement of MPEG video compression

Traditional data compression algorithms for 2D images work using the information theoretic paradigm, attempting to reduce redundant information by as much as possible. However, through the use of a depletion algorithm that takes advantage of characteristics of the human visual system, images can be displayed using only half or a quarter of the original information with no appreciable loss of quality.

The characteristics of the human visual system that allows the viewer to perceive a higher rate of information than is actually displayed is known as the beta or picket fence effect. It is called the picket fence effect because its effect is noticeable when a person is travelling along a picket fence. Despite the person not having an unimpeded view of the objects behind the fence at any instant, as the person is moving, the objects behind the picket fence are clearly visible. In fact, in most cases the fence is hardly noticeable at all.

The techniques we have developed uses this effect to achieve higher levels of compression than would otherwise be possible. As a fundamental characteristic of the beta effect is the requirement that there is movement of the fence in relation to the object, the beta effect can only be used in image sequences where movement between the depletion pattern and objects within the image can be achieved.

As MPEG is the recognised standard by which image sequences are coded, compatibility with MPEG is essential. We have modified our technique such that it performs in conjunction with MPEG, providing further compression over MPEG.

Two-dimensional string notation for representing video sequences

Most current work on video indexing concentrates on queries which operate over high level semantic information which must be entirely composed and entered manually. We propose an indexing system which is based on spatial information about key objects in a scene. These key objects may be detected automatically, with manual supervision, and tracked through a sequence using one of a number of recently developed techniques. This representation is highly compact and allows rapid resolution of queries specified by iconic example. A number of systems have been produced which use 2D string notations to index digital image libraries. Just as 2D strings provide a compact and tractable indexing notation for digital pictures, a sequence of 2D strings might provide an index for a video or image sequence. To improve further upon this we reduce the representation to the 2D string pair representing the initial frame, and a sequence of edits to these strings. This takes advantage of the continuity between frames to further reduce the size of the notation. By representing video sequences using string edits, a notation has been developed which is compact, and allows querying on the spatial relationships of objects to be performed without rebuilding the majority of the scene. Calculating ranks of objects directly from the edit sequence allows matching with minimal calculation, thus greatly reducing search time. This paper presents the edit sequence notation and algorithms for evaluating queries over image sequences. A number of optimizations which represent a considerably saving in search time is demonstrated in the paper.

A unified 2D-3D video scene change detection framework for mobile camera platforms

In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

Temporal semantic compression for video browsing

We present a video browsing approach, termed Temporal Semantic Compression (TSC), that uses automated measures of interest to support today's foraging behaviours. Conventional browsers 'compress' a video stream using simple 2x or 8x fast-forward. TSC browsers dynamically filter video based on a single user gesture to leave out more or less of the boring bits. We demonstrate a browser with an example interest measure, derived from an automated estimate of movie tempo, to forage in terms of narrative structures such as crises, climaxes, and action sequence book-ends. Media understanding algorithms facilitate browsing, and interactivity enables the human-in-the-loop to cope when those algorithms fail to cross the semantic gap.

Passenger monitoring in moving bus video

In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems.

Calibration of audio-video sensors for multi-modal event indexing

This paper addresses the coordinated use of video and audio cues to capture and index surveillance events with multimodal labels. The focus of this paper is the development of a joint-sensor calibration technique that uses audio-visual observations to improve the calibration process. One significant feature of this approach is the ability to continuously check and update the calibration status of the sensor suite, making it resilient to independent drift in the individual sensors. We present scenarios in which this system is used to enhance surveillance.

Finding the optimal temporal partitioning of video sequences

The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.

Segmentation of intentional human gestures for sports video annotation

We present results on the recognition of intentional human gestures for video annotation and retrieval. We define a gesture as a particular, repeatable, human movement having a predefined meaning. An obvious application of the work is in sports video annotation where umpire gestures indicate specific events. Our approach is to augment video with data obtained from accelerometers worn as wrist bands by one or more officials. We present the recognition performance using a Hidden Markov Model approach for gesture modeling with both isolated gestures and gestures segmented from a stream.