The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86 overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.
A recently interesting work I--pic @cite_0 allows people to broadcast their privacy preferences and appearance information to nearby devices using BLE. This work can be incorporated into our framework. Furthermore, we specify context elements that have not been considered before, such as scene and presence of others. Besides, we provide a convenient mechanism for people to temporarily change their privacy preferences using hand gestures when facing the camera, while broadcasted data in I-pic may not be received by people who take images, or the received data is outdated.
@cite_0
2467391961 2139763424 2620105270 2142514943
The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware. In a crowded public space, people often walk in groups, either with people they know or strangers. Associating a group of people over space and time can assist understanding individual’s behaviours as it provides vital visual context for matching individuals within the group. Seemingly an ‘easier’ task compared with person matching given more and richer visual content, this problem is in fact very challenging because a group of people can be highly non-rigid with changing relative position of people within the group and severe self-occlusions. In this paper, for the first time, the problem of matching associating groups of people over large space and time captured in multiple non-overlapping camera views is addressed. Specifically, a novel people group representation and a group matching algorithm are proposed. The former addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views. In addition, we demonstrate a notable enhancement on individual person matching by utilising the group description as visual context. Our methods are validated using the 2008 i-LIDS Multiple-Camera Tracking Scenario (MCTS) dataset on multiple camera views from a busy airport arrival hall. In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is substantially faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new MPII Video Pose dataset of image sequences with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes. Presence-sharing is an emerging platform for mobile applications, but presence-privacy remains a challenge. Privacy controls must be flexible enough to allow sharing between both trusted social relations and untrusted strangers. In this paper, we present a system called SmokeScreen that provides flexible and power-efficient mechanisms for privacy management. Broadcasting clique signals, which can only be interpreted by other trusted users, enables sharing between social relations; broadcasting opaque identifiers (OIDs), which can only be resolved to an identity by a trusted broker, enables sharing between strangers. Computing these messages is power-efficient since they can be pre-computed with acceptable storage costs. In evaluating these mechanisms we first analyzed traces from an actual presence-sharing application. Four months of traces provide evidence of anonymous snooping, even among trusted users. We have also implemented our mechanisms on two devices and found the power demands of clique signals and OIDs to be reasonable. A mobile phone running our software can operate for several days on a single charge.