• DeepinView
• High quality imaging with 4 MP resolution
• Excellent low-light performance via DarkFighter technology
• Clear imaging against strong back light due to 140 dB WDR technology
• Efficient H.265+ compression technology to save bandwidth and storage
• Advanced streaming technology that enables smooth live view and data self-correcting in poor network
• 5 streams and up to 5 custom streams to meet a wide variety of applications
• Water and dust resistant (IP67) and vandal proof (IK10)
• Anti-IR reflection bubble guarantees image quality

Face Counting:
With the embedded deep learning algorithms, the camera integrates multiple intelligences. It counts persons and samples the face features simultaneously, and compares them with the built-in face library, so to remove the duplicated person. It counts persons and reports a face alarm simultaneously to achieve both the entrance control and people counting. Hard Hat Detection:
With the embedded deep learning algorithms, the camera detects the persons in the specified region. It detects whether the person is wearing a hard hat, and captures the head of the person and reports an alarm if not. Multi-Target-Type Detection:
With the embedded deep learning algorithms, the camera detects and captures the face and human body in the specified region and outputs the features, such as gender, age and top color. It supports a structurized modeling of the face and human body to achieve a structurized data collection. Queue Management:
With embedded deep learning based algorithms, the camera detects queuing-up people number and waiting time of each person. The body feature detection algorithm helps filter out wrong targets and increase the accuracy of detection. Metadata:
Metadata uses individual instances of application data or the data content. Metadata can be used for third-party application development. Smooth Streaming:
Smooth streaming offers solutions to improve the video quality in different network conditions. For example, in poor network conditions, adapting to the detected real-time network condition, streaming bit rate and resolution are automatically adjusted to avoid mosaic and lower latency in live view; in multiplayer network conditions, the camera transmits the redundant data for the self-error correcting in back-end device, so that to solve the mosaic problem because of the packet loss and error rate. Anti-Corrosion Coating(-Y):
The camera adopts high weatherproof and wearproof molding powder that confirms to AAMA2604-02 and Qualicoat (Class2) specifications, and uses two-layer special spraying technology, so its surface has excellent long-term anti-corrosion performance and physical protection function. Besides, all the exposed screws use SUS316 material and features high anti-corrosion performance. Therefore, the whole camera meets anti-corrosion requirements NEMA 250-2018. 

• High quality imaging with 8 MP resolution
• Excellent low-light performance via DarkFighter technology
• Clear imaging against strong back light due to 120 dB true WDR technology
• Efficient H.265+ compression technology to save bandwidth and storage
• 5 streams to meet a wide variety of applications
• Water and dust resistant (IP67) and vandal proof (IK10)

Face Recognition:
With embedded deep learning based algorithms, the camera is able to give the best shot of a target face through detecting, capturing, grading and selecting. The camera uses face exposure function to dynamically adjust face area exposure of captures and ensures high face picture quality. Perimeter Protection:
With embedded deep learning based target detection and classification algorithms, the camera carries out the duty of perimeter protection, monitoring the actions of line crossing, intrusion, region entrance, and region exiting. The algorithms greatly filter out the mistaken alarm caused by the interference of leafs, lights, animal, flag, etc. Multi-Target-Type Detection:
With the embedded deep learning algorithms, the camera detects and captures the face, human body, vehicle in the specified region. Queue Management:
With embedded deep learning based algorithms, the camera detects queuing-up people number and waiting time of each person. It can generate reports to compare the efficiency of different queuing-ups and display the changing status of one queue, and supports raw data export for further analysis. Regional People Counting:
With the embedded deep learning algorithms, the camera supports people density detection and will upload detection data through scheduled uploading, number of people change uploading and congestion level uploading. It also supports number of people exception detection and waiting time exception detection. On/Off Duty Detection:
With the embedded deep learning algorithms, the camera supports absence detection and on/off duty detection. It can detect the on/off duty status and people number changes in a predefined area. Heat Map:
The camera can generate a graphic description of visits (by calculating amount of people or amount of dwell time) in a configured area. People Counting:
With the embedded deep learning algorithms, the camera integrates multiple intelligences. It counts persons and reports a face alarm simultaneously to achieve both the entrance control and people counting. Hard Hat Detection:
With the embedded deep learning algorithms, the camera detects the persons in the specified region. It detects whether the person is wearing a hard hat, and reports an alarm if not.