Wireless cameras dominate the home surveillance market, providing an additional layer of security for homeowners. Cameras are not limited to private residences; retail stores, public bathrooms, and public beaches represent only some of the possible locations where wireless cameras may be monitoring people's movements. When cameras are deployed into an environment, one would typically expect the user to disclose the presence of the camera as well as its location, which should be outside of a private area. However, adversarial camera users may withhold information and prevent others from discovering the camera, forcing others to determine if they are being recorded on their own. To uncover hidden cameras, a wireless camera detection system must be developed that will recognize the camera's network traffic characteristics. We monitor the network traffic within the immediate area using a separately developed packet sniffer, a program that observes and collects information about network packets. We analyze and classify these packets based on how well their patterns and features match those expected of a wireless camera. We used a Support Vector Machine classifier and a secondary-level of classification to reduce false positives to design and implement a system that uncovers the presence of hidden wireless cameras within an area. / Master of Science / Wireless cameras may be found almost anywhere, whether they are used to monitor city traffic and report on travel conditions or to act as home surveillance when residents are away. Regardless of their purpose, wireless cameras may observe people wherever they are, as long as a power source and Wi-Fi connection are available. While most wireless camera users install such devices for peace of mind, there are some who take advantage of cameras to record others without their permission, sometimes in compromising positions or places. Because of this, systems are needed that may detect hidden wireless cameras. We develop a system that monitors network traffic packets, specifically based on their packet lengths and direction, and determines if the properties of the packets mimic those of a wireless camera stream. A double-layered classification technique is used to uncover hidden wireless cameras and filter out non-wireless camera devices.

In Underwater Acoustic Sensor Networks (UWASNs), cameras have recently been deployed for enhanced monitoring. However, their use has faced several obstacles. Since video capturing and processing consume significant amounts of camera battery power, they are kept in sleep mode and activated only when ultrasonic sensors detect a target. The present study proposes a camera relocation structure in UWASNs to maximize the coverage of detected targets with the least possible vertical camera movement. This approach determines the coverage of each acoustic sensor in advance by getting the most applicable cameras in terms of orientation and frustum of camera in 3-D that are covered by such sensors. Whenever a target is exposed, this information is then used and shared with other sensors that detected the same target. Compared to a flooding-based approach, experiment results indicate that this proposed solution can quickly capture the detected targets with the least camera movement.

<p>Pervasive cameras are generating videos at an unprecedented pace, making videos the new frontier of big data. As the processors, e.g., CPU/GPU, become increasingly powerful, the cloud and edge nodes can generate useful insights from colossal video data. However, as the research in computer vision (CV) develops vigorously, the system area has been a blind spot in CV research. With colossal video data generated from cameras every day and limited compute resource budgets, how to design software systems to generate insights from video data efficiently?</p><p><br></p><p>Designing cost-efficient video analytics software systems is challenged by the expensive computation of vision operators, the colossal data volume, and the precious wireless bandwidth of surveillance cameras. To address above challenges, three software systems are proposed in this thesis. For the first system, we present VStore, a data store that supports fast, resource-efficient analytics over large archival videos. VStore manages video ingestion, storage, retrieval, and consumption and controls video formats through backward derivation of configuration: in the opposite direction along the video data path, VStore passes the video quantity and quality expected by analytics backward to retrieval, to storage, and to ingestion. VStore derives an optimal set of video formats, optimizes for different resources in a progressive manner, and runs queries as fast as 362x of video realtime. For the second system, we present a camera/cloud runtime called DIVA that supports querying cold videos distributed on low-cost wireless cameras. DIVA is built upon a novel zero-streaming paradigm: to save wireless bandwidth, when capturing video frames, a camera builds sparse yet accurate landmark frames without uploading any video data; when executing a query, a camera processes frames in multiple passes with increasingly more expensive operators. On diverse queries over 15 videos, DIVA runs at more than 100x realtime and outperforms competitive alternatives remarkably. For the third system, we present Clique, a practical object re-identification (ReID) engine that builds upon two unconventional techniques. First, Clique assesses target occurrences by clustering unreliable object features extracted by ReID algorithms, with each cluster representing the general impression of a distinct object to be matched against the input. Second, to search across camera videos, Clique samples cameras to maximize the spatiotemporal coverage and incrementally adds cameras for processing on demand. Through evaluation on 25 hours of traffic videos from 25 cameras, Clique reaches a high recall at 5 of 0.87 across 70 queries and runs at 830x of video realtime in achieving high accuracy.</p>