Complex event processing (CEP) is widely employed to detect occurrences of predefined combinations (patterns) of events in massive data streams. As new events are accepted, they are matched using some type of evaluation structure, commonly optimized according to the statistical properties of the data items in the input stream. However, in many real-life scenarios the data characteristics are never known in advance or are subject to frequent on-the-fly changes. To modify the evaluation structure as a reaction to such changes, adaptation mechanisms are employed. These mechanisms typically function by monitoring a set of properties and applying a new evaluation plan when significant deviation from the initial values is observed. This strategy often leads to missing important input changes or it may incur substantial computational overhead by over-adapting. In this paper, we present an efficient and precise method for dynamically deciding whether and how the evaluation structure should be reoptimized. This method is based on a small set of constraints to be satisfied by the monitored values, defined such that a better evaluation plan is guaranteed if any of the constraints is violated. To the best of our knowledge, our proposed mechanism is the first to provably avoid false positives on reoptimization decisions. We formally prove this claim and demonstrate how our method can be applied on known algorithms for evaluation plan generation. Our extensive experimental evaluation on real-world datasets confirms the superiority of our strategy over existing methods in terms of performance and accuracy.
The majority of the proposed CEP techniques are deprived from adaptivity considerations @cite_23 . The two notable exceptions, ZStream @cite_32 and tree-based NFA @cite_25 were covered in detail above. Additional works labeled as 'adaptive' refer to on-the-fly switching between several detection algorithms @cite_19 @cite_43 or dynamic rule mining @cite_49 @cite_33 .
@cite_33 @cite_32 @cite_19 @cite_43 @cite_23 @cite_49 @cite_25
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Among increasingly complicated trackers in visual tracking area, recently proposed correlation filter based trackers have achieved appealing performance despite their great simplicity and superior speed. However, the filter input is a bounding box of fixed size, so they are not born with the adaptability to target’s scale and aspect ratio changes. Although scaleadaptive variants have been proposed, they are not flexible enough due to pre-defined scale sampling manners. Moreover, to the best of our knowledge, no correlation filter variant has been proposed to handle aspect ratio variation. To tackle this problem, this paper integrates the class-agnostic detection proposal method, which is widely adopted in object detection area, into a correlation filter tracker, and presents KCFDP tracker. The correlation filter part of KCFDP is based on KCF[2] with some modifications. We extend the HOG feature in KCF to a combination of HOG, intensity, and color naming by simply concatenating the three features, resulting in 42 feature channels. The model updating scheme in KCF, which is simple linear interpolation, is substituted with a more robust scheme presented in [1]. EdgeBoxes[4] is adopted to generate flexible detection proposals and enable the scale and aspect ratio adaptability of our tracker. It traverses the whole image in a sliding window manner, and scores every sampled bounding box according to the number of contours that are wholly enclosed. To accelerate EdgeBoxes and produce less unnecessary proposals, we set the minimum proposal area and aspect ratio range dynamically in sliding window sampling according to the current target size. In the tracking pipeline, KCF is firstly performed to estimate the preliminary target location ld . Within a patch zd extracted from current frame, KCF locates the target center according to the location of the maximum element in f : f(zd) = kxz d · α, (1) Composite (or Complex) event processing (CEP) systems search sequences of incoming events for occurrences of user-specified event patterns. Recently, they have gained more attention in a variety of areas due to their powerful and expressive query language and performance potential. Sequentiality (temporal ordering) is the primary way in which CEP systems relate events to each other. In this paper, we present a CEP system called ZStream to efficiently process such sequential patterns. Besides simple sequential patterns, ZStream is also able to detect other patterns, including conjunction, disjunction, negation and Kleene closure. Unlike most recently proposed CEP systems, which use non-deterministic finite automata (NFA's) to detect patterns, ZStream uses tree-based query plans for both the logical and physical representation of query patterns. By carefully designing the underlying infrastructure and algorithms, ZStream is able to unify the evaluation of sequence, conjunction, disjunction, negation, and Kleene closure as variants of the join operator. Under this framework, a single pattern in ZStream may have several equivalent physical tree plans, with different evaluation costs. We propose a cost model to estimate the computation costs of a plan. We show that our cost model can accurately capture the actual runtime behavior of a plan, and that choosing the optimal plan can result in a factor of four or more speedup versus an NFA based approach. Based on this cost model and using a simple set of statistics about operator selectivity and data rates, ZStream is able to adaptively and seamlessly adjust the order in which it detects patterns on the fly. Finally, we describe a dynamic programming algorithm used in our cost model to efficiently search for an optimal query plan for a given pattern. We study the use of domain adaptation and transfer learning techniques as part of a framework for adaptive object detection. Unlike recent applications of domain adaptation work in computer vision, which generally focus on image classification, we explore the problem of extreme class imbalance present when performing domain adaptation for object detection. The main difficulty caused by this imbalance is that test images contain millions or billions of negative image subwindows but just a few image subwindows containing positive instances, which makes it difficult to adapt to changes in the positive classes present new domains by simple techniques such as random sampling. We propose an initial approach to addressing this problem and apply our technique to vehicle detection in a challenging urban surveillance dataset, demonstrating the performance of our approach with various amounts of supervision, including the fully unsupervised case. It has been an underlying assumption behind most of the machine learning algorithms that training and test instances should be sampled from a similar distribution. The difference in resolution, difference in view points, clutter and background are the main reasons which can cause the problem of domain shift. Indeed, the problem is particularly pertinent to the computer vision community due to our reliance on ‘standard’ challenging datasets. However most of the work in this field revolves around adapting a classifier for the task of object recognition or classification and not much effort has been put to adapt an object detector. In this paper, we propose subspace alignment based domain adaptation of the state of the art RCNN based object detector [2]. In subspace based domain adaptation for objects, we need access to source and target subspaces for the bounding box features . The absence of supervision (labels and bounding boxes are absent) makes the task challenging. The proposed approach builds upon the previously proposed subspace alignment based method [1] for visual domain adaptation to adapt the RCNN detector [2].