We describe a system for content-based retrieval from large surveillance video archives, using behavior, action and appearance of objects. Objects are detected, tracked, and classified into broad categories. Their behavior and appearance are characterized by action detectors and descriptors, which are indexed in an archive. Queries can be posed as video exemplars, and the results can be refined through relevance feedback. The contributions of our system include the fusion of behavior and action detectors with appearance for matching; the improvement of query results through interactive query refinement (IQR), which learns a discriminative classifier online based on user feedback; and reasonable performance on low resolution, poor quality video. The system operates on video from ground cameras and aerial platforms, both RGB and IR. Performance is evaluated on publicly-available surveillance datasets, showing that subtle actions can be detected under difficult conditions, with reasonable improvement from IQR.

The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom (PTZ) camera network in order to maximize various scene understanding performance criteria (e.g., tracking accuracy, best shot, and image resolution) through dynamic camera-to-target assignment and efficient feature acquisition. Moreover, we consider the situation where processing is distributed across the network since it is often unrealistic to have all the image data at a central location. In such situations, the cameras, although autonomous, must collaborate among themselves because each camera's PTZ parameter entails constraints on the others. Motivated by recent work in cooperative control of sensor networks, we propose a distributed optimization strategy, which can be modeled as a game involving the cameras and targets. The cameras gain by reducing the error covariance of the tracked targets or through higher resolution feature acquisition, which, however, comes at the risk of losing the dynamic target. Through the optimization of this reward-versus-risk tradeoff, we are able to control the PTZ parameters of the cameras and assign them to targets dynamically. The tracks, upon which the control algorithm is dependent, are obtained through a consensus estimation algorithm whereby cameras can arrive at a consensus on the state of each target through a negotiation strategy. We analyze the performance of this collaborative sensing strategy in active camera networks in a simulation environment, as well as a real-life camera network.

Summary form only given. We present a concept for automatic construction site monitoring by taking into account 4D information (3D over time), that is acquired from highly-overlapping digital aerial images. On the one hand today's maturity of flying micro aerial vehicles (MAVs) enables a low-cost and an efficient image acquisition of high-quality data that maps construction sites entirely from many varying viewpoints. On the other hand, due to low-noise sensors and high redundancy in the image data, recent developments in 3D reconstruction workflows have benefited the automatic computation of accurate and dense 3D scene information. Having both an inexpensive high-quality image acquisition and an efficient 3D analysis workflow enables monitoring, documentation and visualization of observed sites over time with short intervals. Relating acquired 4D site observations, composed of color, texture, geometry over time, largely supports automated methods toward full scene understanding, the acquisition of both the change and the construction site's progress.

We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.

Videos usually consist of activities involving interactions between multiple actors, sometimes referred to as complex activities. Recognition of such activities requires modeling the spatio-temporal relationships between the actors and their individual variabilities. In this paper, we consider the problem of recognition of complex activities in a video given a query example. We propose a new feature model based on a string representation of the video which respects the spatio-temporal ordering. This ordered arrangement of local collections of features (e.g., cuboids, STIP), which are the characters in the string, are initially matched using graph-based spectral techniques. Final recognition is obtained by matching the string representations of the query and the test videos in a dynamic programming framework which allows for variability in sampling rates and speed of activity execution. The method does not require tracking or recognition of body parts, is able to identify the region of interest in a cluttered scene, and gives reasonable performance with even a single query example. We test our approach in an example-based video retrieval framework with two publicly available complex activity datasets and provide comparisons against other methods that have studied this problem.

In this work, we consider a camera network where processing is distributed across the cameras. Our goal is to recognize actions of multiple targets consistently observed over the entire network. To obtain consistent and better results we need to properly fuse the action scores from multiple cameras. There have been multiple works on distributed tracking and distributed data association for multiple targets in a camera network. We can use the data association results and tracking confidence scores to improve the action recognition results. We propose a consensus based framework for solving this problem in an integrated manner and with a completely distributed camera network architecture. We propose a novel method for weighting the action scores based on tracking confidences and show how the cameras can reach a consensus about the action of a target using belief consensus. We show real life experiments and performance metrics with multiple cameras and targets.

Maintaining the stability of tracks on multiple targets in video over extended time periods remains a challenging problem. A few methods which have recently shown encouraging results in this direction rely on learning context models or the availability of training data. However, this may not be feasible in many application scenarios. Moreover, tracking methods should be able to work across multiple resolutions of the video. In this paper, we consider the problem of long-term tracking in video in application domains where context information is not available a priori, nor can it be learned online. We build our solution on the hypothesis that most existing trackers can obtain reasonable short-term tracks (tracklets). By analyzing the statistical properties of these tracklets, we develop associations between them so as to come up with longer tracks. On multiple real-life video sequences spanning low and high resolution data, we show the ability to accurately track over extended time periods.

Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.

This paper deals with the problem of tracking multiple targets in a distributed network of self-configuring pan-tilt-zoom cameras. We focus on applications where events unfold over a large geographic area and need to be analyzed by multiple overlapping and non-overlapping active cameras without a central unit accumulating and analyzing all the data. The overall goal is to keep track of all targets in the region of deployment of the cameras, while selectively focusing at a high resolution on some particular target features. To acquire all the targets at the desired resolutions while keeping the entire scene in view, we use cooperative network control ideas based on multi-player learning in games. For tracking the targets as they move through the area covered by the cameras, we propose a special application of the distributed estimation algorithm known as Kalman-Consensus filter through which each camera comes to a consensus with its neighboring cameras about the actual state of the target. This leads to a camera network topology that changes with time. Combining these ideas with single-view analysis, we have a completely distributed approach for multi-target tracking and camera network self-configuration. We show performance analysis results with real-life experiments on a network of 10 cameras.

We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-linear dynamical feedback system. A composite feature vector describing the shape, color and motion of the objects, and a non-linear, piecewise stationary, stochastic dynamical model describing its spatio-temporal evolution, are used for tracking. The tracking error or expected log likelihood, which serves as a feedback signal, is used to automatically detect changes and switch between activities happening one after another in a long video sequence. Whenever a change is detected, the tracker is re initialized automatically by comparing the input image with learned models of the activities. Unlike some other approaches that can track a sequence of activities, we do not need to know the transition probabilities between the activities, which can be difficult to estimate in many application scenarios. We demonstrate the effectiveness of the method on multiple indoor and outdoor real-life videos and analyze its performance.

We present a novel stochastic, adaptive strategy for tracking multiple people in a large network of video cameras. Similarities between features (appearance and biometrics) observed at different cameras are continuously adapted and the stochastically optimal path for each person computed. The following are the major contributions of the proposed approach. First, we consider situations where the feature similarities are uncertain and treat them as random variables. We show how the distributions of these random variables can be learned and how to compute the tracks in a stochastically optimal manner. Second, we consider the possibility of long-term interdependence of the features over space and time. This allows us to adoptively evolve the feature correspondences by observing the system performance over a time window, and correct for errors in the similarity computations. Third, we show that the above two conditions can be addressed by treating the issue of tracking in a camera network as an optimization problem in a stochastic adaptive system. We show results on data collected by a large camera network. The proposed approach is particularly suitable for distributed processing over the entire network.

Recently, 'entry/exit' events of objects in the field-of-views of cameras were used to learn the topology of the camera network. The integration of object appearance was also proposed to employ the visual information provided by the imaging sensors. A problem with these methods is the lack of robustness to appearance changes. This paper integrates face recognition in the statistical model to better estimate the correspondence in the time-varying network. The statistical dependence between the entry and exit nodes indicates the connectivity and traffic patterns of the camera network, which are represented by a weighted directed graph and transition time distributions. A nine-camera network with 25 nodes is analyzed both in simulation and in real-life experiments, and compared with the previous approaches.

We present a novel distributed video coding algorithm based on transform coding of distributed sources and exploiting the geometrical relationships between the location of the sensors. The geometry is used to align the video sequences and distributed quantization of transform coefficients is used to eliminate spatial and inter-sensor redundancy. In contrast with most of the current video compression standards which only exploit spatial and temporal dundancy within each video sequence, we also consider the significant redundancy between the sequences. Results demonstrate that our algorithm yields a significant saving in bit rate on the overlapping portion of multiple views.

We present a novel 3D model-based distributed video coding algorithm. It is based on independent, model-based tracking of multiple sources and distributed coding of the tracked feature points. The model-based tracking scheme provides correspondence between the overlapping set of features that are visible in the different views. While the motion estimates obtained from the tracking algorithm remove temporal redundancy and the 3D model accounts for removing spatial redundancy, distributed coding is used to eliminate inter-sensor redundancy. Thus, in contrast to most of the current video compression standards which only exploit spatial and temporal redundancy within each video sequence, we also consider the significant redundancy between the sequences. Results demonstrate that our algorithm yields a significant saving in bit rate on the overlapping portion of multiple views.

In order to summarize a video consisting of a sequence of different activities, there are three fundamental problems: tracking the objects of interest, detecting the activity change times and recognizing the new activity. This paper presents an algorithm for achieving all these three tasks simultaneously and presents results on how it can used for indexing and summarizing a real-life video sequence. Human activities are represented by a model for the dynamics of the shape of the human body contour. Measures are designed for detecting both gradual transitions and sudden changes between activity models.