Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle

Abstract

Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target's motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

“…For complex applications, however, imbedding proposed models in current security systems becomes necessary, such as compressive sensing for sparse tracking [18] (it can be improved as locally compressive sensing within ROI), VIBE algorithm for real-time object detection from a moving camera [19], Adaboost algorithm for noise-detection in ROI [20], optical flow for robots' recognition of environments [21], SVM clustering for accidents classification [22], deep learning algorithms for anomaly detection, crow analysis, and hierarchical tracking within ROI [23][24][25][26][27]. Objects understanding and detection in dynamic environment changes are usually based on the adaptive background subtraction and other objects recognition methods [17,21,35,[65][66][67][68].…”

“…Numerous algorithms have been developed to tackle video recognition challenges in various environments; however, a full understanding of environmental implications to video recognition efficiency demands learning models with universal significance (ignoring uncontrolled differences in real scenarios) [18][19][20][21][22][23][24][25][26][27]. That is the essential reason why the current online algorithms, even for latest algorithms, for example, the latest models for tackling crowd segmentation for the high-dimensional, large-scale anomaly detection, still encounter considerable uncertainties [23,24].…”

“…How to evaluate and compute the regulated attention in implications of the surrounding environment changes and, furthermore, how to define the warning level of EC-implications to video recognition efficiency should be research priorities towards precision security in intelligent surveillance [21][22][23][24][25][26][27]. It has been widely recognized that video surveillance should consider the implications of surrounding environments changes to video surveillance, but until now, there are still no models for a universal evaluation of EC-implications to video recognition efficiency [4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. To solve the unresolved issues associated with uncontrolled EC-implications, various novel optimization models were proposed and further applied in current learning systems [13][14][15].…”

New trends on moving object detection in video images captured by a moving camera: A survey

“…Indeed, it is more effective to exploit target specific representations through a learning process rather than using a fixed set of pre-defined features [178]. Deep neural networks, especially convolutional neural networks (CNN) have recently proposed for moving object tracking which effectively use category specific features for tracking and show some promising results even in the case of complex moving camera [179,180,181].…”

Deep Learning of Appearance Models for Online Object Tracking

“…Although the currently used hand-crafted features produce acceptable tracking results, it is always preferred to leverage more descriptive features. Therefore, it is more beneficial to exploit target-specific representations through a learning process rather than using a fixed set of pre-defined features [23].…”