Abstract: A camera device with a quick-release function includes a camera lens, a circuit board and a bridging module. The camera lens has an optical axis. The circuit board has an optical sensor aligning with the optical axis of the camera lens. The bridging module is used to assemble the camera lens with the circuit board. The bridging module includes a lens holder and a glue holder. The lens holder is used to hold the camera lens. The glue holder is detachably disposed on the circuit board, and can be adhered to the lens holder via glue so as to constrain a relative movement between the camera lens and the circuit board.

Abstract: An image mode application method is applied to a surveillance apparatus with an operation processor. The operation processor receives an image stream having a first detection image and a second detection image generated in sequence. The image mode application method includes detecting a first feature of a first target object within the first detection image, switching an application mode of the surveillance apparatus from a first mode to a second mode in accordance with a detection result of the first feature, detecting a second feature of the first target object within the second detection image, determining whether the second feature conforms to the first feature, and switching the application mode from the second mode to the first mode when the second features conforms to the first feature.

Abstract: A floor height detection method is applied to a surveillance camera, and a surveillance image captured by the surveillance camera contains a surveillance region. The floor height detection method includes dividing the surveillance image at least into a first region and a second region, acquiring an actual installing height matrix of the surveillance camera upon the first region and the second region, and utilizing a matrix factorization algorithm to analyze the actual installing height matrix to acquire floor heights within the surveillance region.

Abstract: An object identification method is applied to a surveillance system. The surveillance system includes at least one surveillance apparatus. The object identification method includes acquiring a plurality of first feature vectors of a first moving object and a plurality of second feature vectors of at least one second moving object within a series of surveillance images from the surveillance apparatus, transforming the first feature vectors and the second feature vectors respectively into a first cluster distribution set and at least one second cluster distribution set, comparing similarity of the first cluster distribution set and the at least one second cluster distribution set, and setting a similarity ranking of the first cluster distribution set and the at least one second cluster distribution set according to a comparison result so as to determine whether the first moving object and the at least one second moving object are the same.

Abstract: An automatic rule setting method is applied to an image content analysis apparatus. The image content analysis apparatus includes an operation processor and an image receiver. The image receiver is adapted to receive a surveillance image. The operation processor executes the automatic rule setting method. The automatic rule setting method includes analyzing the surveillance image to acquire a scene datum, determining whether the scene datum conforms to a detection rule in accordance with a predefined condition, and automatically drawing a detection boundary of the detection rule on a target region of the surveillance image corresponding to the scene datum when the scene datum conforms to the detection rule, so as to utilize the detection boundary to acquire an object behavior parameter relevant to the detection boundary.

Abstract: A resolution detection device is applied to a camera device and includes at least one arc-shaped structural member and an identification member. The at least one arc-shaped structural member is disposed on a position distant from a detection point in a predefined distance. The identification member includes a plurality of identification patterns. The identification member is formed on the at least one arc-shaped structural member, and a plurality of intervals between the detection point and each of the plurality of identification patterns is set as the predefined distance. The camera device acquires a captured image via the detection point, and the captured image contains the plurality of identification patterns. The resolution detection device further includes a relay lens disposed on the detection point.

Abstract: An image analysis method is applied to an image analysis apparatus having an operation processor and an image receiver. The image analysis method includes setting a range provided by an original image as a reference image, and dividing the reference image into a plurality of first auxiliary images in accordance with a valid size. The plurality of first auxiliary images is applied for an image analysis model to generate an image classification result. The operation processor divides the base image acquired by the image receiver into a plurality of second auxiliary images in accordance with the valid size, and the plurality of second auxiliary images is applied for the image analysis model to decide a number of the plurality of first auxiliary images.

Abstract: A camera angle detection method is applied to a surveillance apparatus and includes detecting a plurality of straight lines within a surveillance image acquired by the surveillance apparatus, selecting at least one first candidate parallel line and at least one second candidate parallel line from the plurality of straight lines according to a directional trend, stretching the first candidate parallel line and the second candidate parallel line to acquire an intersection point, and computing parameter difference between the intersection point and a reference point of the surveillance image so as to transform the parameter difference to acquire a tilted angle of an optical axis of a lens of the surveillance apparatus relative to a supporting plane where the surveillance apparatus is located.

Abstract: An adjustable seat includes a mount, a rotary base, a rotary fixing frame, a fixing frame transmission mechanism, and a fixing frame driving module. The rotary base is rotatably connected to the mount and is rotatable around a first rotation axis relative to the mount. The rotary fixing frame is rotatably connected to the rotary base and is rotatable around a second rotation axis relative to the rotary base. The fixing frame transmission mechanism is rotatably connected to the rotary fixing frame. The fixing frame driving module is disposed on the mount and connects with the fixing frame transmission mechanism. The fixing frame driving module drives the rotary fixing frame through the fixing frame transmission mechanism to rotate around the second rotation axis relative to the rotary base. A camera includes the adjustable seat and an image-capturing module fixed on the rotary fixing frame for capturing images.

Abstract: A surveillance region identifying method is used to analyze a region feature of a surveillance region covered by a surveillance apparatus. The surveillance region identifying method includes analyzing all track information within a series of images acquired by the surveillance apparatus to acquire an appearing point and a disappearing point of each track information, utilizing cluster analysis to define a main appearing point cluster of the appearing points, computing enter vectors of all appearing points inside the main appearing point cluster, and analyzing vector angles of all enter vectors of the main appearing point cluster to define an entrance of the surveillance region.

Abstract: A 3D image mask analysis method is applied to an image surveillance apparatus having an image receiver and an operation processor. The 3D image mask analysis method includes establishing a 3D image mask with first depth information inside a surveillance image acquired by the image receiver, utilizing an object identification technology to acquire second depth information of a target object at least partly overlapped with the 3D image mask inside the surveillance image, and comparing the first depth information to the second depth information so as to determine whether an image of the target object is displayed inside the surveillance image in accordance with a comparison result.

Abstract: A sheltering mechanism applied to a surveillance camera and includes a sheltering component and a resilient component. The casing includes an opening. The sheltering component is slidably disposed on a casing to shelter an opening formed on the casing. The opening is surrounded by the resilient component. The resilient component includes a base portion, a bridging portion and a contacting portion. The base is fixed to the casing. The bridging component is connected to the base portion and stretches outwardly from the base portion. The contacting portion is disposed on an end of the bridging portion opposite to the base portion. The bridging portion pushes the contacting portion to contact against the sheltering component for sealing a gap between the casing and the sheltering component.

Abstract: A tension adjustment mechanism applied to a surveillance camera and includes a base, a positioning component, a first actuating component and a first fixing component. The base includes a first slot and first hole. The positioning component is slidably disposed on the base. The first actuating component has a first fixed end and a first free end opposite to each other. The first fixed end is fixedly disposed on the positioning component. The first free end has an inclined guiding structure and is movably inserted into the first slot. The first fixing component abuts against the inclined guiding structure and is movably locked with the first hole.

Abstract: An object sampling method is applied to an image analysis apparatus with an image receiver and an operation processor. The image receiver acquires an image stream. The object sampling method includes transforming color space of at least one surveillance image in the image stream, computing saturation and gradient of specific pixels within the at least one surveillance image, setting a bonding box within the at least one surveillance image, and computing a background score relevant to the saturation and value of all pixels in the bonding box, and comparing the background score with a first sampling condition so as to determine whether the at least one surveillance image is applicable for the image analysis apparatus via a comparison result of comparing the foreground score with the first sampling condition.

Abstract: An image analysis model adjustment method is applied to an image analysis apparatus. The image analysis model adjustment method includes dividing a detection image acquired by the imager analysis apparatus into a plurality of first sub-images via a valid size, transforming the plurality of first sub-images from a spatial domain to a frequency domain to generate a plurality of first frequency domain images, distributing the plurality of first frequency domain images into several crop groups via a predefined set value, analyzing frequency responses at the same frequency in each crop group to provide a specific frequency response, collecting specific frequency responses of the crop groups to generate frequency domain group data, and analyzing the frequency domain group data to acquire a prediction result of the detection image so as to decide whether to adjust the valid size in accordance with the predicted result.

Abstract: An image analysis method is applied to a camera apparatus and used to analyze the landform inside a detection image captured by the camera apparatus via a predefined installation angle. The image analysis method includes dividing the detection image into a plurality of first regions to acquire a plurality of first height, defining some first regions with the same height and adjacent to each other as a first reference plane area to acquire a first plane formula, selecting at least one first region adjacent to and outside the first reference plane area, applying the selected first region for the first plane formula to acquire a first planar height and comparing the first planar height with an initial first height of the at least one first region, and determining landform between the first reference plane area and the selected at least one first region in accordance with a comparison result.